Overview

Dataset statistics

Number of variables64
Number of observations6087
Missing cells33479
Missing cells (%)8.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 MiB
Average record size in memory520.0 B

Variable types

Numeric13
Categorical24
DateTime2
Boolean25

Alerts

asset_backed has constant value ""Constant
oid has constant value ""Constant
slob has constant value ""Constant
issue_offered_global has constant value ""Constant
unit_deal has constant value ""Constant
form_of_own has constant value ""Constant
bond_type has constant value ""Constant
greater_of has constant value ""Constant
lesser_of has constant value ""Constant
prospectus_issuer_name has a high cardinality: 1069 distinct valuesHigh cardinality
issuer_cusip has a high cardinality: 1078 distinct valuesHigh cardinality
issue_cusip has a high cardinality: 1471 distinct valuesHigh cardinality
issue_name has a high cardinality: 227 distinct valuesHigh cardinality
delivery_date has a high cardinality: 1744 distinct valuesHigh cardinality
isin has a high cardinality: 6080 distinct valuesHigh cardinality
complete_cusip has a high cardinality: 6087 distinct valuesHigh cardinality
effective_date has a high cardinality: 1767 distinct valuesHigh cardinality
dated_date has a high cardinality: 1761 distinct valuesHigh cardinality
first_interest_date has a high cardinality: 1583 distinct valuesHigh cardinality
last_interest_date has a high cardinality: 2515 distinct valuesHigh cardinality
issue_id is highly overall correlated with amount_outstanding and 3 other fieldsHigh correlation
gross_spread is highly overall correlated with selling_concession and 3 other fieldsHigh correlation
selling_concession is highly overall correlated with gross_spread and 3 other fieldsHigh correlation
offering_price is highly overall correlated with comp_neg_exch_deal and 1 other fieldsHigh correlation
offering_yield is highly overall correlated with gross_spread and 3 other fieldsHigh correlation
principal_amt is highly overall correlated with subsequent_data and 2 other fieldsHigh correlation
action_price is highly overall correlated with mtn and 6 other fieldsHigh correlation
action_amount is highly overall correlated with mtn and 4 other fieldsHigh correlation
amount_outstanding is highly overall correlated with issue_idHigh correlation
coupon is highly overall correlated with gross_spread and 2 other fieldsHigh correlation
years_to_maturity is highly overall correlated with gross_spread and 2 other fieldsHigh correlation
coupon_type is highly overall correlated with interest_frequency and 2 other fieldsHigh correlation
mtn is highly overall correlated with action_price and 4 other fieldsHigh correlation
yankee is highly overall correlated with sec_reg_type1 and 1 other fieldsHigh correlation
canadian is highly overall correlated with sec_reg_type1High correlation
comp_neg_exch_deal is highly overall correlated with offering_price and 2 other fieldsHigh correlation
rule_415_reg is highly overall correlated with issue_idHigh correlation
sec_reg_type1 is highly overall correlated with offering_price and 3 other fieldsHigh correlation
denomination is highly overall correlated with yankeeHigh correlation
defeased is highly overall correlated with action_price and 4 other fieldsHigh correlation
defaulted is highly overall correlated with action_price and 2 other fieldsHigh correlation
tender_exch_offer is highly overall correlated with action_typeHigh correlation
refund_protection is highly overall correlated with action_price and 4 other fieldsHigh correlation
overallotment_opt is highly overall correlated with selling_concession and 2 other fieldsHigh correlation
active_issue is highly overall correlated with issue_id and 2 other fieldsHigh correlation
subsequent_data is highly overall correlated with principal_amt and 4 other fieldsHigh correlation
action_type is highly overall correlated with tender_exch_offer and 2 other fieldsHigh correlation
see_note is highly overall correlated with offering_yield and 8 other fieldsHigh correlation
interest_frequency is highly overall correlated with coupon_type and 2 other fieldsHigh correlation
pay_in_kind is highly overall correlated with issue_id and 7 other fieldsHigh correlation
coupon_change_indicator is highly overall correlated with coupon_type and 2 other fieldsHigh correlation
day_count_basis is highly overall correlated with coupon_type and 2 other fieldsHigh correlation
issue_name is highly imbalanced (59.8%)Imbalance
security_level is highly imbalanced (88.6%)Imbalance
mtn is highly imbalanced (99.6%)Imbalance
canadian is highly imbalanced (86.0%)Imbalance
settlement_type is highly imbalanced (98.9%)Imbalance
comp_neg_exch_deal is highly imbalanced (71.7%)Imbalance
sec_reg_type1 is highly imbalanced (54.1%)Imbalance
denomination is highly imbalanced (71.7%)Imbalance
covenants is highly imbalanced (81.5%)Imbalance
defeased is highly imbalanced (99.8%)Imbalance
defaulted is highly imbalanced (99.8%)Imbalance
redeemable is highly imbalanced (52.3%)Imbalance
refund_protection is highly imbalanced (99.8%)Imbalance
overallotment_opt is highly imbalanced (99.4%)Imbalance
announced_call is highly imbalanced (95.8%)Imbalance
dep_eligibility is highly imbalanced (94.6%)Imbalance
see_note is highly imbalanced (98.6%)Imbalance
interest_frequency is highly imbalanced (79.1%)Imbalance
pay_in_kind is highly imbalanced (99.6%)Imbalance
coupon_change_indicator is highly imbalanced (72.4%)Imbalance
day_count_basis is highly imbalanced (59.3%)Imbalance
gross_spread has 348 (5.7%) missing valuesMissing
selling_concession has 1206 (19.8%) missing valuesMissing
offering_price has 100 (1.6%) missing valuesMissing
offering_yield has 1058 (17.4%) missing valuesMissing
subsequent_data has 2506 (41.2%) missing valuesMissing
action_price has 4998 (82.1%) missing valuesMissing
action_amount has 4746 (78.0%) missing valuesMissing
greater_of has 5322 (87.4%) missing valuesMissing
lesser_of has 5322 (87.4%) missing valuesMissing
see_note has 5322 (87.4%) missing valuesMissing
pay_in_kind has 2502 (41.1%) missing valuesMissing
offering_yield is highly skewed (γ1 = 70.91490893)Skewed
principal_amt is highly skewed (γ1 = 61.2777128)Skewed
action_amount is highly skewed (γ1 = 36.31222246)Skewed
isin is uniformly distributedUniform
complete_cusip is uniformly distributedUniform
issue_id has unique valuesUnique
complete_cusip has unique valuesUnique
offering_price has 204 (3.4%) zerosZeros
amount_outstanding has 2079 (34.2%) zerosZeros

Reproduction

Analysis started2023-08-14 16:50:52.823346
Analysis finished2023-08-14 16:51:24.735891
Duration31.91 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

issue_id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct6087
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean766994.93
Minimum570533
Maximum1042547
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.1 KiB
2023-08-14T18:51:24.870304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum570533
5-th percentile583824
Q1634829
median744749
Q3895369
95-th percentile1010416.8
Maximum1042547
Range472014
Interquartile range (IQR)260540

Descriptive statistics

Standard deviation144304.64
Coefficient of variation (CV)0.18814289
Kurtosis-1.2858222
Mean766994.93
Median Absolute Deviation (MAD)124640
Skewness0.32786087
Sum4.6686981 × 109
Variance2.082383 × 1010
MonotonicityNot monotonic
2023-08-14T18:51:25.029051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
570539 1
 
< 0.1%
849247 1
 
< 0.1%
849765 1
 
< 0.1%
849579 1
 
< 0.1%
849577 1
 
< 0.1%
849575 1
 
< 0.1%
849551 1
 
< 0.1%
849545 1
 
< 0.1%
849531 1
 
< 0.1%
849259 1
 
< 0.1%
Other values (6077) 6077
99.8%
ValueCountFrequency (%)
570533 1
< 0.1%
570535 1
< 0.1%
570539 1
< 0.1%
570547 1
< 0.1%
570549 1
< 0.1%
570551 1
< 0.1%
570553 1
< 0.1%
570577 1
< 0.1%
570581 1
< 0.1%
570587 1
< 0.1%
ValueCountFrequency (%)
1042547 1
< 0.1%
1042546 1
< 0.1%
1042545 1
< 0.1%
1041125 1
< 0.1%
1040943 1
< 0.1%
1040942 1
< 0.1%
1040939 1
< 0.1%
1040864 1
< 0.1%
1040832 1
< 0.1%
1040686 1
< 0.1%

issuer_id
Real number (ℝ)

Distinct1038
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26958.803
Minimum13
Maximum51608
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.1 KiB
2023-08-14T18:51:25.164402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile534
Q13879
median35354
Q342177
95-th percentile48129
Maximum51608
Range51595
Interquartile range (IQR)38298

Descriptive statistics

Standard deviation18425.671
Coefficient of variation (CV)0.68347511
Kurtosis-1.5643658
Mean26958.803
Median Absolute Deviation (MAD)10275
Skewness-0.43132715
Sum1.6409823 × 108
Variance3.3950534 × 108
MonotonicityNot monotonic
2023-08-14T18:51:25.301784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39095 104
 
1.7%
32904 98
 
1.6%
1664 72
 
1.2%
4525 64
 
1.1%
263 61
 
1.0%
47149 56
 
0.9%
42313 49
 
0.8%
46381 48
 
0.8%
3271 48
 
0.8%
1255 46
 
0.8%
Other values (1028) 5441
89.4%
ValueCountFrequency (%)
13 2
 
< 0.1%
41 10
0.2%
74 7
 
0.1%
86 3
 
< 0.1%
104 2
 
< 0.1%
121 6
 
0.1%
169 22
0.4%
187 15
0.2%
231 9
0.1%
241 12
0.2%
ValueCountFrequency (%)
51608 4
0.1%
51560 2
 
< 0.1%
51541 3
< 0.1%
51540 1
 
< 0.1%
51533 2
 
< 0.1%
51525 2
 
< 0.1%
51400 2
 
< 0.1%
51384 1
 
< 0.1%
51380 1
 
< 0.1%
51289 6
0.1%
Distinct1069
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
AT&T INC
 
104
CITIGROUP INC
 
98
WESTPAC BKG CORP
 
64
APPLE INC
 
61
JPMORGAN CHASE & CO
 
56
Other values (1064)
5704 

Length

Max length58
Median length49
Mean length17.9655
Min length5

Characters and Unicode

Total characters109356
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique293 ?
Unique (%)4.8%

Sample

1st rowAT&T INC
2nd rowSYNOVUS FINL CORP
3rd rowBMC SOFTWARE INC
4th rowAT&T INC
5th rowAFLAC INC

Common Values

ValueCountFrequency (%)
AT&T INC 104
 
1.7%
CITIGROUP INC 98
 
1.6%
WESTPAC BKG CORP 64
 
1.1%
APPLE INC 61
 
1.0%
JPMORGAN CHASE & CO 56
 
0.9%
GENERAL MTRS FINL CO INC 49
 
0.8%
SUMITOMO MITSUI FINL GROUP INC 48
 
0.8%
PEPSICO INC 48
 
0.8%
BANK NOVA SCOTIA B C 46
 
0.8%
DISNEY WALT CO 46
 
0.8%
Other values (1059) 5467
89.8%

Length

2023-08-14T18:51:25.455114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc 2610
 
12.9%
corp 1444
 
7.1%
co 896
 
4.4%
group 396
 
2.0%
395
 
2.0%
finl 339
 
1.7%
new 274
 
1.4%
plc 258
 
1.3%
cap 247
 
1.2%
fin 246
 
1.2%
Other values (1322) 13132
64.9%

Most occurring characters

ValueCountFrequency (%)
14150
12.9%
C 9477
 
8.7%
N 8665
 
7.9%
I 8093
 
7.4%
O 7680
 
7.0%
E 7470
 
6.8%
R 7191
 
6.6%
A 6494
 
5.9%
L 5309
 
4.9%
T 5142
 
4.7%
Other values (29) 29685
27.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 94548
86.5%
Space Separator 14150
 
12.9%
Other Punctuation 532
 
0.5%
Decimal Number 97
 
0.1%
Dash Punctuation 29
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 9477
 
10.0%
N 8665
 
9.2%
I 8093
 
8.6%
O 7680
 
8.1%
E 7470
 
7.9%
R 7191
 
7.6%
A 6494
 
6.9%
L 5309
 
5.6%
T 5142
 
5.4%
S 5102
 
5.4%
Other values (16) 23925
25.3%
Decimal Number
ValueCountFrequency (%)
6 24
24.7%
3 17
17.5%
0 16
16.5%
2 15
15.5%
1 15
15.5%
4 6
 
6.2%
5 4
 
4.1%
Other Punctuation
ValueCountFrequency (%)
& 433
81.4%
/ 93
 
17.5%
, 5
 
0.9%
. 1
 
0.2%
Space Separator
ValueCountFrequency (%)
14150
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 94548
86.5%
Common 14808
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 9477
 
10.0%
N 8665
 
9.2%
I 8093
 
8.6%
O 7680
 
8.1%
E 7470
 
7.9%
R 7191
 
7.6%
A 6494
 
6.9%
L 5309
 
5.6%
T 5142
 
5.4%
S 5102
 
5.4%
Other values (16) 23925
25.3%
Common
ValueCountFrequency (%)
14150
95.6%
& 433
 
2.9%
/ 93
 
0.6%
- 29
 
0.2%
6 24
 
0.2%
3 17
 
0.1%
0 16
 
0.1%
2 15
 
0.1%
1 15
 
0.1%
4 6
 
< 0.1%
Other values (3) 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 109356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14150
12.9%
C 9477
 
8.7%
N 8665
 
7.9%
I 8093
 
7.4%
O 7680
 
7.0%
E 7470
 
6.8%
R 7191
 
6.6%
A 6494
 
5.9%
L 5309
 
4.9%
T 5142
 
4.7%
Other values (29) 29685
27.1%

issuer_cusip
Categorical

Distinct1078
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
00206R
 
104
172967
 
95
345397
 
72
961214
 
64
037833
 
61
Other values (1073)
5691 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters36522
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique304 ?
Unique (%)5.0%

Sample

1st row00206R
2nd row87161C
3rd row055921
4th row00206R
5th row001055

Common Values

ValueCountFrequency (%)
00206R 104
 
1.7%
172967 95
 
1.6%
345397 72
 
1.2%
961214 64
 
1.1%
037833 61
 
1.0%
46647P 56
 
0.9%
37045X 49
 
0.8%
86562M 48
 
0.8%
713448 48
 
0.8%
254687 46
 
0.8%
Other values (1068) 5444
89.4%

Length

2023-08-14T18:51:25.579140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00206r 104
 
1.7%
172967 95
 
1.6%
345397 72
 
1.2%
961214 64
 
1.1%
037833 61
 
1.0%
46647p 56
 
0.9%
37045x 49
 
0.8%
86562m 48
 
0.8%
713448 48
 
0.8%
254687 46
 
0.8%
Other values (1068) 5444
89.4%

Most occurring characters

ValueCountFrequency (%)
0 4144
11.3%
6 3671
10.1%
4 3667
10.0%
2 3651
10.0%
7 3402
9.3%
3 3332
9.1%
5 3327
9.1%
1 3264
8.9%
8 2996
8.2%
9 2467
6.8%
Other values (24) 2601
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33921
92.9%
Uppercase Letter 2601
 
7.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 227
 
8.7%
R 204
 
7.8%
X 191
 
7.3%
V 175
 
6.7%
H 166
 
6.4%
Y 160
 
6.2%
L 158
 
6.1%
M 136
 
5.2%
N 122
 
4.7%
Q 118
 
4.5%
Other values (14) 944
36.3%
Decimal Number
ValueCountFrequency (%)
0 4144
12.2%
6 3671
10.8%
4 3667
10.8%
2 3651
10.8%
7 3402
10.0%
3 3332
9.8%
5 3327
9.8%
1 3264
9.6%
8 2996
8.8%
9 2467
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 33921
92.9%
Latin 2601
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 227
 
8.7%
R 204
 
7.8%
X 191
 
7.3%
V 175
 
6.7%
H 166
 
6.4%
Y 160
 
6.2%
L 158
 
6.1%
M 136
 
5.2%
N 122
 
4.7%
Q 118
 
4.5%
Other values (14) 944
36.3%
Common
ValueCountFrequency (%)
0 4144
12.2%
6 3671
10.8%
4 3667
10.8%
2 3651
10.8%
7 3402
10.0%
3 3332
9.8%
5 3327
9.8%
1 3264
9.6%
8 2996
8.8%
9 2467
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4144
11.3%
6 3671
10.1%
4 3667
10.0%
2 3651
10.0%
7 3402
9.3%
3 3332
9.1%
5 3327
9.1%
1 3264
8.9%
8 2996
8.2%
9 2467
6.8%
Other values (24) 2601
7.1%

issue_cusip
Categorical

Distinct1471
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
AD3
 
26
AF2
 
24
AC4
 
24
AC5
 
24
AA1
 
23
Other values (1466)
5966 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18261
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique683 ?
Unique (%)11.2%

Sample

1st rowBB7
2nd rowAJ4
3rd rowAB6
4th rowBC5
5th rowAH5

Common Values

ValueCountFrequency (%)
AD3 26
 
0.4%
AF2 24
 
0.4%
AC4 24
 
0.4%
AC5 24
 
0.4%
AA1 23
 
0.4%
AJ4 23
 
0.4%
AA0 23
 
0.4%
AA7 23
 
0.4%
AF9 23
 
0.4%
AP4 22
 
0.4%
Other values (1461) 5852
96.1%

Length

2023-08-14T18:51:25.679105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ad3 26
 
0.4%
ac4 24
 
0.4%
ac5 24
 
0.4%
af2 24
 
0.4%
aa1 23
 
0.4%
aj4 23
 
0.4%
aa0 23
 
0.4%
aa7 23
 
0.4%
af9 23
 
0.4%
aa3 22
 
0.4%
Other values (1461) 5852
96.1%

Most occurring characters

ValueCountFrequency (%)
A 3667
20.1%
B 1565
 
8.6%
C 848
 
4.6%
2 631
 
3.5%
3 626
 
3.4%
4 623
 
3.4%
9 622
 
3.4%
1 621
 
3.4%
5 618
 
3.4%
7 614
 
3.4%
Other values (24) 7826
42.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12137
66.5%
Decimal Number 6124
33.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 3667
30.2%
B 1565
12.9%
C 848
 
7.0%
D 594
 
4.9%
E 459
 
3.8%
F 390
 
3.2%
G 379
 
3.1%
J 378
 
3.1%
H 374
 
3.1%
L 309
 
2.5%
Other values (14) 3174
26.2%
Decimal Number
ValueCountFrequency (%)
2 631
10.3%
3 626
10.2%
4 623
10.2%
9 622
10.2%
1 621
10.1%
5 618
10.1%
7 614
10.0%
8 597
9.7%
0 595
9.7%
6 577
9.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 12137
66.5%
Common 6124
33.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 3667
30.2%
B 1565
12.9%
C 848
 
7.0%
D 594
 
4.9%
E 459
 
3.8%
F 390
 
3.2%
G 379
 
3.1%
J 378
 
3.1%
H 374
 
3.1%
L 309
 
2.5%
Other values (14) 3174
26.2%
Common
ValueCountFrequency (%)
2 631
10.3%
3 626
10.2%
4 623
10.2%
9 622
10.2%
1 621
10.1%
5 618
10.1%
7 614
10.0%
8 597
9.7%
0 595
9.7%
6 577
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18261
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 3667
20.1%
B 1565
 
8.6%
C 848
 
4.6%
2 631
 
3.5%
3 626
 
3.4%
4 623
 
3.4%
9 622
 
3.4%
1 621
 
3.4%
5 618
 
3.4%
7 614
 
3.4%
Other values (24) 7826
42.9%

issue_name
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct227
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
GLOBAL SR NT
2414 
GLOBAL NT
1283 
GLOBAL GTD SR NT
834 
GLOBAL GTD NT
359 
GLOBAL SR NT FLTG RT
 
208
Other values (222)
989 

Length

Max length47
Median length44
Mean length13.650731
Min length2

Characters and Unicode

Total characters83092
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique149 ?
Unique (%)2.4%

Sample

1st rowGLOBAL NT
2nd rowGLOBAL SR NT
3rd rowGLOBAL SR NT
4th rowGLOBAL NT
5th rowGLOBAL SR NT

Common Values

ValueCountFrequency (%)
GLOBAL SR NT 2414
39.7%
GLOBAL NT 1283
21.1%
GLOBAL GTD SR NT 834
 
13.7%
GLOBAL GTD NT 359
 
5.9%
GLOBAL SR NT FLTG RT 208
 
3.4%
GLOBAL NT FLTG RT 172
 
2.8%
GLOBAL SR NT FIXED/FLTG RT 108
 
1.8%
GLOBAL NT FIXED/FLTG RT 53
 
0.9%
GLOBAL 1ST MTG BD 50
 
0.8%
GLOBAL SUB NT FIXED/FLTG RT 47
 
0.8%
Other values (217) 559
 
9.2%

Length

2023-08-14T18:51:25.803557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
global 6051
30.5%
nt 5910
29.8%
sr 3837
19.4%
gtd 1381
 
7.0%
rt 759
 
3.8%
fltg 533
 
2.7%
fixed/fltg 222
 
1.1%
ser 167
 
0.8%
bd 129
 
0.7%
sub 122
 
0.6%
Other values (135) 712
 
3.6%

Most occurring characters

ValueCountFrequency (%)
13736
16.5%
L 12911
15.5%
T 9080
10.9%
G 8318
10.0%
B 6392
7.7%
A 6150
7.4%
O 6092
7.3%
N 5973
7.2%
R 4843
 
5.8%
S 4303
 
5.2%
Other values (29) 5294
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 68677
82.7%
Space Separator 13736
 
16.5%
Decimal Number 387
 
0.5%
Other Punctuation 284
 
0.3%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 12911
18.8%
T 9080
13.2%
G 8318
12.1%
B 6392
9.3%
A 6150
9.0%
O 6092
8.9%
N 5973
8.7%
R 4843
 
7.1%
S 4303
 
6.3%
D 1899
 
2.8%
Other values (16) 2716
 
4.0%
Decimal Number
ValueCountFrequency (%)
1 158
40.8%
2 114
29.5%
0 74
19.1%
8 14
 
3.6%
7 9
 
2.3%
9 6
 
1.6%
5 5
 
1.3%
3 4
 
1.0%
6 3
 
0.8%
Other Punctuation
ValueCountFrequency (%)
/ 258
90.8%
& 26
 
9.2%
Space Separator
ValueCountFrequency (%)
13736
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 68677
82.7%
Common 14415
 
17.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 12911
18.8%
T 9080
13.2%
G 8318
12.1%
B 6392
9.3%
A 6150
9.0%
O 6092
8.9%
N 5973
8.7%
R 4843
 
7.1%
S 4303
 
6.3%
D 1899
 
2.8%
Other values (16) 2716
 
4.0%
Common
ValueCountFrequency (%)
13736
95.3%
/ 258
 
1.8%
1 158
 
1.1%
2 114
 
0.8%
0 74
 
0.5%
& 26
 
0.2%
8 14
 
0.1%
7 9
 
0.1%
- 8
 
0.1%
9 6
 
< 0.1%
Other values (3) 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83092
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13736
16.5%
L 12911
15.5%
T 9080
10.9%
G 8318
10.0%
B 6392
7.7%
A 6150
7.4%
O 6092
7.3%
N 5973
7.2%
R 4843
 
5.8%
S 4303
 
5.2%
Other values (29) 5294
 
6.4%
Distinct2340
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
Minimum2013-09-13 00:00:00
Maximum2052-05-01 00:00:00
2023-08-14T18:51:25.942908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:26.084795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

security_level
Categorical

Distinct6
Distinct (%)0.1%
Missing6
Missing (%)0.1%
Memory size95.1 KiB
SEN
5839 
SS
 
122
SUB
 
104
SENS
 
9
JUNS
 
6

Length

Max length4
Median length3
Mean length2.9824042
Min length2

Characters and Unicode

Total characters18136
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSEN
2nd rowSEN
3rd rowSEN
4th rowSEN
5th rowSEN

Common Values

ValueCountFrequency (%)
SEN 5839
95.9%
SS 122
 
2.0%
SUB 104
 
1.7%
SENS 9
 
0.1%
JUNS 6
 
0.1%
NON 1
 
< 0.1%
(Missing) 6
 
0.1%

Length

2023-08-14T18:51:26.215672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T18:51:26.355102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
sen 5839
96.0%
ss 122
 
2.0%
sub 104
 
1.7%
sens 9
 
0.1%
juns 6
 
0.1%
non 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 6211
34.2%
N 5856
32.3%
E 5848
32.2%
U 110
 
0.6%
B 104
 
0.6%
J 6
 
< 0.1%
O 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 18136
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 6211
34.2%
N 5856
32.3%
E 5848
32.2%
U 110
 
0.6%
B 104
 
0.6%
J 6
 
< 0.1%
O 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 18136
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 6211
34.2%
N 5856
32.3%
E 5848
32.2%
U 110
 
0.6%
B 104
 
0.6%
J 6
 
< 0.1%
O 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18136
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 6211
34.2%
N 5856
32.3%
E 5848
32.2%
U 110
 
0.6%
B 104
 
0.6%
J 6
 
< 0.1%
O 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
False
4696 
True
1391 
ValueCountFrequency (%)
False 4696
77.1%
True 1391
 
22.9%
2023-08-14T18:51:26.468820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

coupon_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size95.1 KiB
F
5314 
V
772 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6086
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 5314
87.3%
V 772
 
12.7%
(Missing) 1
 
< 0.1%

Length

2023-08-14T18:51:26.566088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T18:51:26.670069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
f 5314
87.3%
v 772
 
12.7%

Most occurring characters

ValueCountFrequency (%)
F 5314
87.3%
V 772
 
12.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6086
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 5314
87.3%
V 772
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 6086
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 5314
87.3%
V 772
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6086
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 5314
87.3%
V 772
 
12.7%

mtn
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
False
6085 
True
 
2
ValueCountFrequency (%)
False 6085
> 99.9%
True 2
 
< 0.1%
2023-08-14T18:51:26.761531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
False
6087 
ValueCountFrequency (%)
False 6087
100.0%
2023-08-14T18:51:26.848370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

yankee
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
False
5139 
True
948 
ValueCountFrequency (%)
False 5139
84.4%
True 948
 
15.6%
2023-08-14T18:51:26.934327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

canadian
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
False
5967 
True
 
120
ValueCountFrequency (%)
False 5967
98.0%
True 120
 
2.0%
2023-08-14T18:51:27.028551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

oid
Boolean

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
False
6087 
ValueCountFrequency (%)
False 6087
100.0%
2023-08-14T18:51:27.433542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

slob
Boolean

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
False
6087 
ValueCountFrequency (%)
False 6087
100.0%
2023-08-14T18:51:27.511457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
True
6087 
ValueCountFrequency (%)
True 6087
100.0%
2023-08-14T18:51:27.592666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

settlement_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
S
6081 
N
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6087
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 6081
99.9%
N 6
 
0.1%

Length

2023-08-14T18:51:27.673356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T18:51:27.770959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
s 6081
99.9%
n 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 6081
99.9%
N 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6087
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 6081
99.9%
N 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 6087
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 6081
99.9%
N 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6087
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 6081
99.9%
N 6
 
0.1%

gross_spread
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct137
Distinct (%)2.4%
Missing348
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean5.020101
Minimum0.35
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.1 KiB
2023-08-14T18:51:27.874227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.35
5-th percentile1.5
Q13.5
median4.5
Q36.5
95-th percentile8.75
Maximum30
Range29.65
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.619326
Coefficient of variation (CV)0.52176759
Kurtosis7.1586594
Mean5.020101
Median Absolute Deviation (MAD)2
Skewness1.7130547
Sum28810.36
Variance6.8608687
MonotonicityNot monotonic
2023-08-14T18:51:28.004298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.5 1359
22.3%
4.5 688
11.3%
3.5 647
10.6%
6 498
 
8.2%
2.5 463
 
7.6%
4 285
 
4.7%
3 174
 
2.9%
2 173
 
2.8%
6.25 169
 
2.8%
1.5 149
 
2.4%
Other values (127) 1134
18.6%
(Missing) 348
 
5.7%
ValueCountFrequency (%)
0.35 2
 
< 0.1%
0.4 1
 
< 0.1%
0.5 5
 
0.1%
0.6 1
 
< 0.1%
0.65 1
 
< 0.1%
0.7 1
 
< 0.1%
0.75 5
 
0.1%
0.8 11
 
0.2%
0.9 1
 
< 0.1%
1 77
1.3%
ValueCountFrequency (%)
30 1
 
< 0.1%
28.333 1
 
< 0.1%
22.5 2
 
< 0.1%
20 7
0.1%
19.6 1
 
< 0.1%
19.53 1
 
< 0.1%
19 1
 
< 0.1%
18.4 1
 
< 0.1%
17.927 1
 
< 0.1%
17.86 1
 
< 0.1%

selling_concession
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct94
Distinct (%)1.9%
Missing1206
Missing (%)19.8%
Infinite0
Infinite (%)0.0%
Mean2.9216462
Minimum0.08
Maximum28.333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.1 KiB
2023-08-14T18:51:28.141334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile1
Q12
median3
Q34
95-th percentile4.5
Maximum28.333
Range28.253
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3039729
Coefficient of variation (CV)0.44631446
Kurtosis33.519418
Mean2.9216462
Median Absolute Deviation (MAD)1
Skewness2.0589071
Sum14260.555
Variance1.7003454
MonotonicityNot monotonic
2023-08-14T18:51:28.266815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 1247
20.5%
2 613
10.1%
2.5 516
8.5%
3.5 444
 
7.3%
1.5 416
 
6.8%
3 244
 
4.0%
3.75 190
 
3.1%
1 177
 
2.9%
5 118
 
1.9%
4.5 81
 
1.3%
Other values (84) 835
13.7%
(Missing) 1206
19.8%
ValueCountFrequency (%)
0.08 1
 
< 0.1%
0.2 2
 
< 0.1%
0.25 1
 
< 0.1%
0.3 4
 
0.1%
0.35 1
 
< 0.1%
0.4 3
 
< 0.1%
0.45 5
 
0.1%
0.48 5
 
0.1%
0.5 16
 
0.3%
0.6 57
0.9%
ValueCountFrequency (%)
28.333 1
 
< 0.1%
18 1
 
< 0.1%
12.533 1
 
< 0.1%
10 3
< 0.1%
9 2
 
< 0.1%
8.75 1
 
< 0.1%
8.25 1
 
< 0.1%
7.5 6
0.1%
7 5
0.1%
6.75 4
0.1%

comp_neg_exch_deal
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
NEG
5787 
EXCH
 
300

Length

Max length4
Median length3
Mean length3.0492854
Min length3

Characters and Unicode

Total characters18561
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNEG
2nd rowNEG
3rd rowNEG
4th rowNEG
5th rowNEG

Common Values

ValueCountFrequency (%)
NEG 5787
95.1%
EXCH 300
 
4.9%

Length

2023-08-14T18:51:28.382117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T18:51:28.483428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
neg 5787
95.1%
exch 300
 
4.9%

Most occurring characters

ValueCountFrequency (%)
E 6087
32.8%
N 5787
31.2%
G 5787
31.2%
X 300
 
1.6%
C 300
 
1.6%
H 300
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 18561
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 6087
32.8%
N 5787
31.2%
G 5787
31.2%
X 300
 
1.6%
C 300
 
1.6%
H 300
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 18561
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 6087
32.8%
N 5787
31.2%
G 5787
31.2%
X 300
 
1.6%
C 300
 
1.6%
H 300
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18561
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 6087
32.8%
N 5787
31.2%
G 5787
31.2%
X 300
 
1.6%
C 300
 
1.6%
H 300
 
1.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
True
3675 
False
2412 
ValueCountFrequency (%)
True 3675
60.4%
False 2412
39.6%
2023-08-14T18:51:28.575120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

sec_reg_type1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)0.2%
Missing32
Missing (%)0.5%
Memory size95.1 KiB
S-3
2637 
RBNA
2611 
F-3
483 
S-4
 
230
F-4
 
38
Other values (7)
 
56

Length

Max length4
Median length3
Mean length3.4351775
Min length2

Characters and Unicode

Total characters20800
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowRBNA
2nd rowRBNA
3rd rowS-3
4th rowRBNA
5th rowRBNA

Common Values

ValueCountFrequency (%)
S-3 2637
43.3%
RBNA 2611
42.9%
F-3 483
 
7.9%
S-4 230
 
3.8%
F-4 38
 
0.6%
F-10 28
 
0.5%
F-6 9
 
0.1%
F-9 7
 
0.1%
S-1 5
 
0.1%
NR 4
 
0.1%
Other values (2) 3
 
< 0.1%
(Missing) 32
 
0.5%

Length

2023-08-14T18:51:28.672706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s-3 2637
43.6%
rbna 2611
43.1%
f-3 483
 
8.0%
s-4 230
 
3.8%
f-4 38
 
0.6%
f-10 28
 
0.5%
f-6 9
 
0.1%
f-9 7
 
0.1%
s-1 5
 
0.1%
nr 4
 
0.1%
Other values (2) 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
- 3440
16.5%
3 3120
15.0%
S 2875
13.8%
R 2615
12.6%
N 2615
12.6%
B 2611
12.6%
A 2611
12.6%
F 565
 
2.7%
4 268
 
1.3%
1 33
 
0.2%
Other values (5) 47
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 13892
66.8%
Decimal Number 3468
 
16.7%
Dash Punctuation 3440
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 3120
90.0%
4 268
 
7.7%
1 33
 
1.0%
0 28
 
0.8%
6 9
 
0.3%
9 7
 
0.2%
2 2
 
0.1%
8 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
S 2875
20.7%
R 2615
18.8%
N 2615
18.8%
B 2611
18.8%
A 2611
18.8%
F 565
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 3440
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13892
66.8%
Common 6908
33.2%

Most frequent character per script

Common
ValueCountFrequency (%)
- 3440
49.8%
3 3120
45.2%
4 268
 
3.9%
1 33
 
0.5%
0 28
 
0.4%
6 9
 
0.1%
9 7
 
0.1%
2 2
 
< 0.1%
8 1
 
< 0.1%
Latin
ValueCountFrequency (%)
S 2875
20.7%
R 2615
18.8%
N 2615
18.8%
B 2611
18.8%
A 2611
18.8%
F 565
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 3440
16.5%
3 3120
15.0%
S 2875
13.8%
R 2615
12.6%
N 2615
12.6%
B 2611
12.6%
A 2611
12.6%
F 565
 
2.7%
4 268
 
1.3%
1 33
 
0.2%
Other values (5) 47
 
0.2%

offering_amt
Real number (ℝ)

Distinct375
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean878446.4
Minimum379
Maximum11000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.1 KiB
2023-08-14T18:51:28.804743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum379
5-th percentile300000
Q1500000
median700000
Q31000000
95-th percentile2000000
Maximum11000000
Range10999621
Interquartile range (IQR)500000

Descriptive statistics

Standard deviation693083.78
Coefficient of variation (CV)0.78898812
Kurtosis29.806007
Mean878446.4
Median Absolute Deviation (MAD)300000
Skewness3.733875
Sum5.3471032 × 109
Variance4.8036512 × 1011
MonotonicityNot monotonic
2023-08-14T18:51:28.940959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500000 959
15.8%
1000000 720
 
11.8%
750000 571
 
9.4%
400000 364
 
6.0%
1250000 328
 
5.4%
300000 316
 
5.2%
600000 305
 
5.0%
1500000 294
 
4.8%
350000 197
 
3.2%
250000 167
 
2.7%
Other values (365) 1866
30.7%
ValueCountFrequency (%)
379 1
< 0.1%
727 1
< 0.1%
8040 1
< 0.1%
11000 1
< 0.1%
13811 1
< 0.1%
21000 1
< 0.1%
21270 1
< 0.1%
27250 1
< 0.1%
29802 1
< 0.1%
30000 1
< 0.1%
ValueCountFrequency (%)
11000000 2
 
< 0.1%
9518964 1
 
< 0.1%
9000000 1
 
< 0.1%
7500000 1
 
< 0.1%
6000000 4
0.1%
5500000 2
 
< 0.1%
5400614 1
 
< 0.1%
5341555 1
 
< 0.1%
5000000 7
0.1%
4750000 1
 
< 0.1%
Distinct1513
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
Minimum2012-02-08 00:00:00
Maximum2022-06-28 00:00:00
2023-08-14T18:51:29.078078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:29.205784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

offering_price
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1049
Distinct (%)17.5%
Missing100
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean96.36492
Minimum0
Maximum110.146
Zeros204
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size95.1 KiB
2023-08-14T18:51:29.347993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile98.684
Q199.644
median99.861
Q399.992
95-th percentile100
Maximum110.146
Range110.146
Interquartile range (IQR)0.348

Descriptive statistics

Standard deviation18.105223
Coefficient of variation (CV)0.18788189
Kurtosis24.377046
Mean96.36492
Median Absolute Deviation (MAD)0.139
Skewness-5.1335679
Sum576936.78
Variance327.79911
MonotonicityNot monotonic
2023-08-14T18:51:29.482895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1398
 
23.0%
0 204
 
3.4%
99.994 25
 
0.4%
99.991 24
 
0.4%
99.929 21
 
0.3%
99.99 20
 
0.3%
99.982 20
 
0.3%
99.83 19
 
0.3%
99.981 19
 
0.3%
99.912 18
 
0.3%
Other values (1039) 4219
69.3%
(Missing) 100
 
1.6%
ValueCountFrequency (%)
0 204
3.4%
90 1
 
< 0.1%
95.273 1
 
< 0.1%
95.291 1
 
< 0.1%
95.304 1
 
< 0.1%
95.444 1
 
< 0.1%
95.83 1
 
< 0.1%
97.052 1
 
< 0.1%
97.084 1
 
< 0.1%
97.09 1
 
< 0.1%
ValueCountFrequency (%)
110.146 1
< 0.1%
106.94884 1
< 0.1%
104.688 1
< 0.1%
102.776 1
< 0.1%
102.002 1
< 0.1%
101.414 1
< 0.1%
101.1867 1
< 0.1%
100.763 1
< 0.1%
100.63342 1
< 0.1%
100.5119 1
< 0.1%

offering_yield
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct4485
Distinct (%)89.2%
Missing1058
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean351.93157
Minimum0.25702
Maximum1750000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.1 KiB
2023-08-14T18:51:29.622706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.25702
5-th percentile1.182478
Q12.302
median3.13045
Q33.91189
95-th percentile5.25
Maximum1750000
Range1749999.7
Interquartile range (IQR)1.60989

Descriptive statistics

Standard deviation24677.283
Coefficient of variation (CV)70.11955
Kurtosis5028.9495
Mean351.93157
Median Absolute Deviation (MAD)0.80455
Skewness70.914909
Sum1769863.8
Variance6.0896829 × 108
MonotonicityNot monotonic
2023-08-14T18:51:29.759809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.375 17
 
0.3%
5 15
 
0.2%
5.25 14
 
0.2%
5.125 13
 
0.2%
5.875 13
 
0.2%
4.75 13
 
0.2%
5.75 12
 
0.2%
5.5 10
 
0.2%
5.625 10
 
0.2%
4.25 9
 
0.1%
Other values (4475) 4903
80.5%
(Missing) 1058
 
17.4%
ValueCountFrequency (%)
0.25702 1
< 0.1%
0.309 1
< 0.1%
0.333 1
< 0.1%
0.34482 1
< 0.1%
0.36387 1
< 0.1%
0.37418 1
< 0.1%
0.3878 1
< 0.1%
0.40085 1
< 0.1%
0.418 1
< 0.1%
0.41914 1
< 0.1%
ValueCountFrequency (%)
1750000 1
< 0.1%
3924 1
< 0.1%
15 1
< 0.1%
12.5 1
< 0.1%
12.0962 1
< 0.1%
10.37761 1
< 0.1%
10.125 1
< 0.1%
9.99964 1
< 0.1%
9.99465 1
< 0.1%
9.75 1
< 0.1%

delivery_date
Categorical

Distinct1744
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
2019-11-25
 
35
2019-06-03
 
24
2019-09-16
 
21
2018-11-26
 
19
2020-03-25
 
18
Other values (1739)
5970 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters60870
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique465 ?
Unique (%)7.6%

Sample

1st row2012-02-13
2nd row2012-02-13
3rd row2012-02-13
4th row2012-02-13
5th row2012-02-10

Common Values

ValueCountFrequency (%)
2019-11-25 35
 
0.6%
2019-06-03 24
 
0.4%
2019-09-16 21
 
0.3%
2018-11-26 19
 
0.3%
2020-03-25 18
 
0.3%
2017-05-11 18
 
0.3%
2020-05-18 16
 
0.3%
2018-12-13 16
 
0.3%
2020-03-27 15
 
0.2%
2015-03-12 15
 
0.2%
Other values (1734) 5890
96.8%

Length

2023-08-14T18:51:29.886036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-11-25 35
 
0.6%
2019-06-03 24
 
0.4%
2019-09-16 21
 
0.3%
2018-11-26 19
 
0.3%
2020-03-25 18
 
0.3%
2017-05-11 18
 
0.3%
2020-05-18 16
 
0.3%
2018-12-13 16
 
0.3%
2020-03-27 15
 
0.2%
2015-03-12 15
 
0.2%
Other values (1734) 5890
96.8%

Most occurring characters

ValueCountFrequency (%)
0 14417
23.7%
- 12174
20.0%
2 11793
19.4%
1 10340
17.0%
3 2197
 
3.6%
5 1833
 
3.0%
9 1793
 
2.9%
8 1757
 
2.9%
6 1655
 
2.7%
7 1472
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48696
80.0%
Dash Punctuation 12174
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14417
29.6%
2 11793
24.2%
1 10340
21.2%
3 2197
 
4.5%
5 1833
 
3.8%
9 1793
 
3.7%
8 1757
 
3.6%
6 1655
 
3.4%
7 1472
 
3.0%
4 1439
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 12174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60870
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14417
23.7%
- 12174
20.0%
2 11793
19.4%
1 10340
17.0%
3 2197
 
3.6%
5 1833
 
3.0%
9 1793
 
2.9%
8 1757
 
2.9%
6 1655
 
2.7%
7 1472
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14417
23.7%
- 12174
20.0%
2 11793
19.4%
1 10340
17.0%
3 2197
 
3.6%
5 1833
 
3.0%
9 1793
 
2.9%
8 1757
 
2.9%
6 1655
 
2.7%
7 1472
 
2.4%

unit_deal
Boolean

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
False
6087 
ValueCountFrequency (%)
False 6087
100.0%
2023-08-14T18:51:29.991922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

form_of_own
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
BE
6087 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters12174
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBE
2nd rowBE
3rd rowBE
4th rowBE
5th rowBE

Common Values

ValueCountFrequency (%)
BE 6087
100.0%

Length

2023-08-14T18:51:30.081823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T18:51:30.193707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
be 6087
100.0%

Most occurring characters

ValueCountFrequency (%)
B 6087
50.0%
E 6087
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12174
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 6087
50.0%
E 6087
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12174
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 6087
50.0%
E 6087
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 6087
50.0%
E 6087
50.0%

denomination
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
2/1
4883 
1/1
608 
200/1
 
433
100/1
 
95
150/1
 
36
Other values (8)
 
32

Length

Max length8
Median length3
Mean length3.1920486
Min length3

Characters and Unicode

Total characters19430
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st row2/1
2nd row2/1
3rd row2/1
4th row2/1
5th row2/1

Common Values

ValueCountFrequency (%)
2/1 4883
80.2%
1/1 608
 
10.0%
200/1 433
 
7.1%
100/1 95
 
1.6%
150/1 36
 
0.6%
5/1 12
 
0.2%
250/1 11
 
0.2%
200/2 3
 
< 0.1%
75/1 2
 
< 0.1%
10/1 1
 
< 0.1%
Other values (3) 3
 
< 0.1%

Length

2023-08-14T18:51:30.279447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2/1 4883
80.2%
1/1 608
 
10.0%
200/1 433
 
7.1%
100/1 95
 
1.6%
150/1 36
 
0.6%
5/1 12
 
0.2%
250/1 11
 
0.2%
200/2 3
 
< 0.1%
75/1 2
 
< 0.1%
10/1 1
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 6824
35.1%
/ 6087
31.3%
2 5334
27.5%
0 1119
 
5.8%
5 64
 
0.3%
7 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13343
68.7%
Other Punctuation 6087
31.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6824
51.1%
2 5334
40.0%
0 1119
 
8.4%
5 64
 
0.5%
7 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 6087
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6824
35.1%
/ 6087
31.3%
2 5334
27.5%
0 1119
 
5.8%
5 64
 
0.3%
7 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6824
35.1%
/ 6087
31.3%
2 5334
27.5%
0 1119
 
5.8%
5 64
 
0.3%
7 2
 
< 0.1%

principal_amt
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1967.1433
Minimum0
Maximum2000000
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size95.1 KiB
2023-08-14T18:51:30.373619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1000
Q11000
median1000
Q31000
95-th percentile1000
Maximum2000000
Range2000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation27929.902
Coefficient of variation (CV)14.198204
Kurtosis4312.2779
Mean1967.1433
Median Absolute Deviation (MAD)0
Skewness61.277713
Sum11974001
Variance7.8007943 × 108
MonotonicityNot monotonic
2023-08-14T18:51:30.471814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1000 6060
99.6%
200000 18
 
0.3%
150000 2
 
< 0.1%
2000 2
 
< 0.1%
5000 2
 
< 0.1%
1 1
 
< 0.1%
0 1
 
< 0.1%
2000000 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1
 
< 0.1%
1000 6060
99.6%
2000 2
 
< 0.1%
5000 2
 
< 0.1%
150000 2
 
< 0.1%
200000 18
 
0.3%
2000000 1
 
< 0.1%
ValueCountFrequency (%)
2000000 1
 
< 0.1%
200000 18
 
0.3%
150000 2
 
< 0.1%
5000 2
 
< 0.1%
2000 2
 
< 0.1%
1000 6060
99.6%
1 1
 
< 0.1%
0 1
 
< 0.1%

covenants
Boolean

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size59.4 KiB
True
5915 
False
 
171
(Missing)
 
1
ValueCountFrequency (%)
True 5915
97.2%
False 171
 
2.8%
(Missing) 1
 
< 0.1%
2023-08-14T18:51:30.585338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

defeased
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
False
6086 
True
 
1
ValueCountFrequency (%)
False 6086
> 99.9%
True 1
 
< 0.1%
2023-08-14T18:51:30.675294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

defaulted
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
False
6086 
True
 
1
ValueCountFrequency (%)
False 6086
> 99.9%
True 1
 
< 0.1%
2023-08-14T18:51:30.764637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
False
5251 
True
836 
ValueCountFrequency (%)
False 5251
86.3%
True 836
 
13.7%
2023-08-14T18:51:30.854697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

redeemable
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
True
5462 
False
625 
ValueCountFrequency (%)
True 5462
89.7%
False 625
 
10.3%
2023-08-14T18:51:30.949364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

refund_protection
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
False
6086 
True
 
1
ValueCountFrequency (%)
False 6086
> 99.9%
True 1
 
< 0.1%
2023-08-14T18:51:31.047199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

overallotment_opt
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
False
6084 
True
 
3
ValueCountFrequency (%)
False 6084
> 99.9%
True 3
 
< 0.1%
2023-08-14T18:51:31.159832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
False
6059 
True
 
28
ValueCountFrequency (%)
False 6059
99.5%
True 28
 
0.5%
2023-08-14T18:51:31.260267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
True
4008 
False
2079 
ValueCountFrequency (%)
True 4008
65.8%
False 2079
34.2%
2023-08-14T18:51:31.353770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

dep_eligibility
Categorical

Distinct6
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size95.1 KiB
DCE
5986 
DTC
 
73
EUCD
 
21
DCED
 
3
DE
 
2

Length

Max length4
Median length3
Mean length3.0037792
Min length2

Characters and Unicode

Total characters18281
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDCE
2nd rowDCE
3rd rowDCE
4th rowDCE
5th rowDCE

Common Values

ValueCountFrequency (%)
DCE 5986
98.3%
DTC 73
 
1.2%
EUCD 21
 
0.3%
DCED 3
 
< 0.1%
DE 2
 
< 0.1%
DFEC 1
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2023-08-14T18:51:31.458284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T18:51:31.583021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
dce 5986
98.4%
dtc 73
 
1.2%
eucd 21
 
0.3%
dced 3
 
< 0.1%
de 2
 
< 0.1%
dfec 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
D 6089
33.3%
C 6084
33.3%
E 6013
32.9%
T 73
 
0.4%
U 21
 
0.1%
F 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 18281
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 6089
33.3%
C 6084
33.3%
E 6013
32.9%
T 73
 
0.4%
U 21
 
0.1%
F 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 18281
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 6089
33.3%
C 6084
33.3%
E 6013
32.9%
T 73
 
0.4%
U 21
 
0.1%
F 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18281
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 6089
33.3%
C 6084
33.3%
E 6013
32.9%
T 73
 
0.4%
U 21
 
0.1%
F 1
 
< 0.1%

bond_type
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
CDEB
6087 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters24348
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCDEB
2nd rowCDEB
3rd rowCDEB
4th rowCDEB
5th rowCDEB

Common Values

ValueCountFrequency (%)
CDEB 6087
100.0%

Length

2023-08-14T18:51:31.683886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T18:51:31.784370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
cdeb 6087
100.0%

Most occurring characters

ValueCountFrequency (%)
C 6087
25.0%
D 6087
25.0%
E 6087
25.0%
B 6087
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 24348
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 6087
25.0%
D 6087
25.0%
E 6087
25.0%
B 6087
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24348
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 6087
25.0%
D 6087
25.0%
E 6087
25.0%
B 6087
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 6087
25.0%
D 6087
25.0%
E 6087
25.0%
B 6087
25.0%

subsequent_data
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.1%
Missing2506
Missing (%)41.2%
Memory size59.4 KiB
True
3181 
False
400 
(Missing)
2506 
ValueCountFrequency (%)
True 3181
52.3%
False 400
 
6.6%
(Missing) 2506
41.2%
2023-08-14T18:51:31.876064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

isin
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct6080
Distinct (%)100.0%
Missing7
Missing (%)0.1%
Memory size95.1 KiB
US00206RBB78
 
1
US30034WAA45
 
1
US44106MAZ59
 
1
US714046AG46
 
1
US60687YBA64
 
1
Other values (6075)
6075 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters72960
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6080 ?
Unique (%)100.0%

Sample

1st rowUS00206RBB78
2nd rowUS87161CAJ45
3rd rowUS055921AB64
4th rowUS00206RBC51
5th rowUS001055AH52

Common Values

ValueCountFrequency (%)
US00206RBB78 1
 
< 0.1%
US30034WAA45 1
 
< 0.1%
US44106MAZ59 1
 
< 0.1%
US714046AG46 1
 
< 0.1%
US60687YBA64 1
 
< 0.1%
US60687YAZ25 1
 
< 0.1%
US60687YBB48 1
 
< 0.1%
US34964CAE66 1
 
< 0.1%
US88947EAU47 1
 
< 0.1%
US72346QAC87 1
 
< 0.1%
Other values (6070) 6070
99.7%
(Missing) 7
 
0.1%

Length

2023-08-14T18:51:31.968738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us00206rbb78 1
 
< 0.1%
us02005nal47 1
 
< 0.1%
us00206rbc51 1
 
< 0.1%
us001055ah52 1
 
< 0.1%
us001055aj19 1
 
< 0.1%
us00206rbd35 1
 
< 0.1%
us489170ac47 1
 
< 0.1%
us35671dav73 1
 
< 0.1%
us35671daw56 1
 
< 0.1%
us35671dau90 1
 
< 0.1%
Other values (6070) 6070
99.8%

Most occurring characters

ValueCountFrequency (%)
U 6360
 
8.7%
S 6326
 
8.7%
0 5349
 
7.3%
4 4905
 
6.7%
2 4879
 
6.7%
6 4825
 
6.6%
7 4619
 
6.3%
3 4568
 
6.3%
5 4538
 
6.2%
1 4507
 
6.2%
Other values (24) 22084
30.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46088
63.2%
Uppercase Letter 26872
36.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 6360
23.7%
S 6326
23.5%
A 3740
13.9%
B 1647
 
6.1%
C 955
 
3.6%
D 671
 
2.5%
E 543
 
2.0%
H 540
 
2.0%
P 484
 
1.8%
L 465
 
1.7%
Other values (14) 5141
19.1%
Decimal Number
ValueCountFrequency (%)
0 5349
11.6%
4 4905
10.6%
2 4879
10.6%
6 4825
10.5%
7 4619
10.0%
3 4568
9.9%
5 4538
9.8%
1 4507
9.8%
8 4169
9.0%
9 3729
8.1%

Most occurring scripts

ValueCountFrequency (%)
Common 46088
63.2%
Latin 26872
36.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 6360
23.7%
S 6326
23.5%
A 3740
13.9%
B 1647
 
6.1%
C 955
 
3.6%
D 671
 
2.5%
E 543
 
2.0%
H 540
 
2.0%
P 484
 
1.8%
L 465
 
1.7%
Other values (14) 5141
19.1%
Common
ValueCountFrequency (%)
0 5349
11.6%
4 4905
10.6%
2 4879
10.6%
6 4825
10.5%
7 4619
10.0%
3 4568
9.9%
5 4538
9.8%
1 4507
9.8%
8 4169
9.0%
9 3729
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 6360
 
8.7%
S 6326
 
8.7%
0 5349
 
7.3%
4 4905
 
6.7%
2 4879
 
6.7%
6 4825
 
6.6%
7 4619
 
6.3%
3 4568
 
6.3%
5 4538
 
6.2%
1 4507
 
6.2%
Other values (24) 22084
30.3%

fungible
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
True
4229 
False
1858 
ValueCountFrequency (%)
True 4229
69.5%
False 1858
30.5%
2023-08-14T18:51:32.073505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

complete_cusip
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct6087
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
00206RBB7
 
1
46647PBE5
 
1
714046AG4
 
1
60687YBA6
 
1
60687YAZ2
 
1
Other values (6082)
6082 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters54783
Distinct characters35
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6087 ?
Unique (%)100.0%

Sample

1st row00206RBB7
2nd row87161CAJ4
3rd row055921AB6
4th row00206RBC5
5th row001055AH5

Common Values

ValueCountFrequency (%)
00206RBB7 1
 
< 0.1%
46647PBE5 1
 
< 0.1%
714046AG4 1
 
< 0.1%
60687YBA6 1
 
< 0.1%
60687YAZ2 1
 
< 0.1%
60687YBB4 1
 
< 0.1%
34964CAE6 1
 
< 0.1%
88947EAU4 1
 
< 0.1%
72346QAC8 1
 
< 0.1%
30034WAB2 1
 
< 0.1%
Other values (6077) 6077
99.8%

Length

2023-08-14T18:51:32.168162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00206rbb7 1
 
< 0.1%
02005nal4 1
 
< 0.1%
00206rbc5 1
 
< 0.1%
001055ah5 1
 
< 0.1%
001055aj1 1
 
< 0.1%
00206rbd3 1
 
< 0.1%
489170ac4 1
 
< 0.1%
35671dav7 1
 
< 0.1%
35671daw5 1
 
< 0.1%
35671dau9 1
 
< 0.1%
Other values (6077) 6077
99.8%

Most occurring characters

ValueCountFrequency (%)
0 4739
 
8.7%
4 4290
 
7.8%
2 4282
 
7.8%
6 4248
 
7.8%
7 4016
 
7.3%
3 3958
 
7.2%
5 3945
 
7.2%
1 3885
 
7.1%
A 3744
 
6.8%
8 3593
 
6.6%
Other values (25) 14083
25.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40045
73.1%
Uppercase Letter 14738
 
26.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 3744
25.4%
B 1652
 
11.2%
C 955
 
6.5%
D 671
 
4.6%
E 543
 
3.7%
H 540
 
3.7%
P 484
 
3.3%
L 467
 
3.2%
G 453
 
3.1%
F 451
 
3.1%
Other values (15) 4778
32.4%
Decimal Number
ValueCountFrequency (%)
0 4739
11.8%
4 4290
10.7%
2 4282
10.7%
6 4248
10.6%
7 4016
10.0%
3 3958
9.9%
5 3945
9.9%
1 3885
9.7%
8 3593
9.0%
9 3089
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common 40045
73.1%
Latin 14738
 
26.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 3744
25.4%
B 1652
 
11.2%
C 955
 
6.5%
D 671
 
4.6%
E 543
 
3.7%
H 540
 
3.7%
P 484
 
3.3%
L 467
 
3.2%
G 453
 
3.1%
F 451
 
3.1%
Other values (15) 4778
32.4%
Common
ValueCountFrequency (%)
0 4739
11.8%
4 4290
10.7%
2 4282
10.7%
6 4248
10.6%
7 4016
10.0%
3 3958
9.9%
5 3945
9.9%
1 3885
9.7%
8 3593
9.0%
9 3089
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4739
 
8.7%
4 4290
 
7.8%
2 4282
 
7.8%
6 4248
 
7.8%
7 4016
 
7.3%
3 3958
 
7.2%
5 3945
 
7.2%
1 3885
 
7.1%
A 3744
 
6.8%
8 3593
 
6.6%
Other values (25) 14083
25.7%

action_type
Categorical

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
I
3417 
IM
1326 
E
471 
B
 
259
T
 
259
Other values (7)
355 

Length

Max length3
Median length1
Mean length1.2640053
Min length1

Characters and Unicode

Total characters7694
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowE
2nd rowE
3rd rowB
4th rowIM
5th rowIM

Common Values

ValueCountFrequency (%)
I 3417
56.1%
IM 1326
 
21.8%
E 471
 
7.7%
B 259
 
4.3%
T 259
 
4.3%
RO 138
 
2.3%
X 107
 
1.8%
REV 60
 
1.0%
P 20
 
0.3%
R 18
 
0.3%
Other values (2) 12
 
0.2%

Length

2023-08-14T18:51:32.279487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i 3417
56.1%
im 1326
 
21.8%
e 471
 
7.7%
b 259
 
4.3%
t 259
 
4.3%
ro 138
 
2.3%
x 107
 
1.8%
rev 60
 
1.0%
p 20
 
0.3%
r 18
 
0.3%
Other values (2) 12
 
0.2%

Most occurring characters

ValueCountFrequency (%)
I 4754
61.8%
M 1326
 
17.2%
E 531
 
6.9%
B 259
 
3.4%
T 259
 
3.4%
R 227
 
3.0%
O 139
 
1.8%
X 107
 
1.4%
V 60
 
0.8%
P 31
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7694
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 4754
61.8%
M 1326
 
17.2%
E 531
 
6.9%
B 259
 
3.4%
T 259
 
3.4%
R 227
 
3.0%
O 139
 
1.8%
X 107
 
1.4%
V 60
 
0.8%
P 31
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 7694
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 4754
61.8%
M 1326
 
17.2%
E 531
 
6.9%
B 259
 
3.4%
T 259
 
3.4%
R 227
 
3.0%
O 139
 
1.8%
X 107
 
1.4%
V 60
 
0.8%
P 31
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 4754
61.8%
M 1326
 
17.2%
E 531
 
6.9%
B 259
 
3.4%
T 259
 
3.4%
R 227
 
3.0%
O 139
 
1.8%
X 107
 
1.4%
V 60
 
0.8%
P 31
 
0.4%

effective_date
Categorical

Distinct1767
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
2019-11-25
 
33
2022-05-20
 
27
2020-05-11
 
21
2021-11-08
 
21
2020-05-07
 
19
Other values (1762)
5966 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters60870
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique572 ?
Unique (%)9.4%

Sample

1st row2014-07-15
2nd row2017-11-09
3rd row2018-11-01
4th row2017-02-15
5th row2017-02-15

Common Values

ValueCountFrequency (%)
2019-11-25 33
 
0.5%
2022-05-20 27
 
0.4%
2020-05-11 21
 
0.3%
2021-11-08 21
 
0.3%
2020-05-07 19
 
0.3%
2019-09-16 19
 
0.3%
2021-03-15 18
 
0.3%
2020-03-26 17
 
0.3%
2020-08-10 17
 
0.3%
2020-03-04 17
 
0.3%
Other values (1757) 5878
96.6%

Length

2023-08-14T18:51:32.388112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-11-25 33
 
0.5%
2022-05-20 27
 
0.4%
2020-05-11 21
 
0.3%
2021-11-08 21
 
0.3%
2020-05-07 19
 
0.3%
2019-09-16 19
 
0.3%
2021-03-15 18
 
0.3%
2020-03-26 17
 
0.3%
2020-08-10 17
 
0.3%
2020-03-04 17
 
0.3%
Other values (1757) 5878
96.6%

Most occurring characters

ValueCountFrequency (%)
0 15050
24.7%
2 12827
21.1%
- 12174
20.0%
1 9552
15.7%
9 2002
 
3.3%
3 1808
 
3.0%
5 1783
 
2.9%
8 1690
 
2.8%
6 1402
 
2.3%
7 1393
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48696
80.0%
Dash Punctuation 12174
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15050
30.9%
2 12827
26.3%
1 9552
19.6%
9 2002
 
4.1%
3 1808
 
3.7%
5 1783
 
3.7%
8 1690
 
3.5%
6 1402
 
2.9%
7 1393
 
2.9%
4 1189
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 12174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60870
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15050
24.7%
2 12827
21.1%
- 12174
20.0%
1 9552
15.7%
9 2002
 
3.3%
3 1808
 
3.0%
5 1783
 
2.9%
8 1690
 
2.8%
6 1402
 
2.3%
7 1393
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15050
24.7%
2 12827
21.1%
- 12174
20.0%
1 9552
15.7%
9 2002
 
3.3%
3 1808
 
3.0%
5 1783
 
2.9%
8 1690
 
2.8%
6 1402
 
2.3%
7 1393
 
2.3%

action_price
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct805
Distinct (%)73.9%
Missing4998
Missing (%)82.1%
Infinite0
Infinite (%)0.0%
Mean103.73529
Minimum0
Maximum151.556
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size95.1 KiB
2023-08-14T18:51:32.508783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile99.0024
Q1100
median102.101
Q3105.616
95-th percentile115.3734
Maximum151.556
Range151.556
Interquartile range (IQR)5.616

Descriptive statistics

Standard deviation7.3294175
Coefficient of variation (CV)0.070655007
Kurtosis45.268332
Mean103.73529
Median Absolute Deviation (MAD)2.101
Skewness-1.4471795
Sum112967.73
Variance53.720361
MonotonicityNot monotonic
2023-08-14T18:51:32.648540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 221
 
3.6%
101 27
 
0.4%
102.75 5
 
0.1%
97.75 3
 
< 0.1%
102.875 3
 
< 0.1%
98 3
 
< 0.1%
104.253 2
 
< 0.1%
103.375 2
 
< 0.1%
100.5 2
 
< 0.1%
100.854 2
 
< 0.1%
Other values (795) 819
 
13.5%
(Missing) 4998
82.1%
ValueCountFrequency (%)
0 1
< 0.1%
50.2595 1
< 0.1%
67.1 1
< 0.1%
79.261 1
< 0.1%
79.526 1
< 0.1%
85 1
< 0.1%
89.5 2
< 0.1%
91.688 1
< 0.1%
92.003 1
< 0.1%
92.016 1
< 0.1%
ValueCountFrequency (%)
151.556 1
< 0.1%
147.153 1
< 0.1%
143.454 1
< 0.1%
141.172 1
< 0.1%
137.022 1
< 0.1%
136.585 1
< 0.1%
134.35 1
< 0.1%
132.757 1
< 0.1%
131.805 1
< 0.1%
131.638 1
< 0.1%

action_amount
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct734
Distinct (%)54.7%
Missing4746
Missing (%)78.0%
Infinite0
Infinite (%)0.0%
Mean692558.09
Minimum0
Maximum2.5 × 108
Zeros45
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size95.1 KiB
2023-08-14T18:51:32.792100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile600.008
Q1172550
median400000
Q3694992
95-th percentile1500000
Maximum2.5 × 108
Range2.5 × 108
Interquartile range (IQR)522442

Descriptive statistics

Standard deviation6832317.1
Coefficient of variation (CV)9.8653343
Kurtosis1325.9439
Mean692558.09
Median Absolute Deviation (MAD)250000
Skewness36.312222
Sum9.287204 × 108
Variance4.6680557 × 1013
MonotonicityNot monotonic
2023-08-14T18:51:32.948120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500000 84
 
1.4%
750000 53
 
0.9%
1000000 50
 
0.8%
300000 46
 
0.8%
0 45
 
0.7%
400000 43
 
0.7%
250000 32
 
0.5%
350000 27
 
0.4%
1250000 26
 
0.4%
600000 24
 
0.4%
Other values (724) 911
 
15.0%
(Missing) 4746
78.0%
ValueCountFrequency (%)
0 45
0.7%
9.744 1
 
< 0.1%
10 1
 
< 0.1%
17 1
 
< 0.1%
20 1
 
< 0.1%
25 1
 
< 0.1%
30 1
 
< 0.1%
102 1
 
< 0.1%
173 1
 
< 0.1%
191 1
 
< 0.1%
ValueCountFrequency (%)
250000000 1
< 0.1%
5385495 1
< 0.1%
4000000 1
< 0.1%
3843488 1
< 0.1%
3510199 1
< 0.1%
3190096 1
< 0.1%
3175908 1
< 0.1%
3000000 1
< 0.1%
2890467 1
< 0.1%
2850000 1
< 0.1%

amount_outstanding
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct633
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean560410.28
Minimum0
Maximum9518964
Zeros2079
Zeros (%)34.2%
Negative0
Negative (%)0.0%
Memory size95.1 KiB
2023-08-14T18:51:33.096452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median450000
Q3800000
95-th percentile1850707
Maximum9518964
Range9518964
Interquartile range (IQR)800000

Descriptive statistics

Standard deviation672732.7
Coefficient of variation (CV)1.2004289
Kurtosis10.911483
Mean560410.28
Median Absolute Deviation (MAD)450000
Skewness2.3088767
Sum3.4112174 × 109
Variance4.5256929 × 1011
MonotonicityNot monotonic
2023-08-14T18:51:33.234503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2079
34.2%
500000 559
 
9.2%
1000000 427
 
7.0%
750000 338
 
5.6%
400000 218
 
3.6%
1250000 194
 
3.2%
600000 192
 
3.2%
1500000 186
 
3.1%
300000 169
 
2.8%
350000 111
 
1.8%
Other values (623) 1614
26.5%
ValueCountFrequency (%)
0 2079
34.2%
379 1
 
< 0.1%
632 1
 
< 0.1%
727 1
 
< 0.1%
812 1
 
< 0.1%
1549 1
 
< 0.1%
2205 1
 
< 0.1%
2512 1
 
< 0.1%
2921 1
 
< 0.1%
3091 1
 
< 0.1%
ValueCountFrequency (%)
9518964 1
 
< 0.1%
5500000 2
 
< 0.1%
5341555 1
 
< 0.1%
5000000 4
0.1%
4500000 1
 
< 0.1%
4250000 2
 
< 0.1%
4000000 5
0.1%
3960082 1
 
< 0.1%
3850000 1
 
< 0.1%
3797310 1
 
< 0.1%

greater_of
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing5322
Missing (%)87.4%
Memory size59.4 KiB
False
765 
(Missing)
5322 
ValueCountFrequency (%)
False 765
 
12.6%
(Missing) 5322
87.4%
2023-08-14T18:51:33.354803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

lesser_of
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing5322
Missing (%)87.4%
Memory size59.4 KiB
False
765 
(Missing)
5322 
ValueCountFrequency (%)
False 765
 
12.6%
(Missing) 5322
87.4%
2023-08-14T18:51:33.443986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

see_note
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.3%
Missing5322
Missing (%)87.4%
Memory size59.4 KiB
False
764 
True
 
1
(Missing)
5322 
ValueCountFrequency (%)
False 764
 
12.6%
True 1
 
< 0.1%
(Missing) 5322
87.4%
2023-08-14T18:51:33.535137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

dated_date
Categorical

Distinct1761
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
2017-05-11
 
18
2020-03-25
 
18
2019-08-15
 
16
2020-05-18
 
16
2019-01-15
 
15
Other values (1756)
6004 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters60870
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique469 ?
Unique (%)7.7%

Sample

1st row2012-02-13
2nd row2012-02-13
3rd row2012-02-13
4th row2012-02-13
5th row2012-02-10

Common Values

ValueCountFrequency (%)
2017-05-11 18
 
0.3%
2020-03-25 18
 
0.3%
2019-08-15 16
 
0.3%
2020-05-18 16
 
0.3%
2019-01-15 15
 
0.2%
2020-05-11 15
 
0.2%
2020-04-01 15
 
0.2%
2019-05-15 15
 
0.2%
2017-02-09 15
 
0.2%
2020-03-27 15
 
0.2%
Other values (1751) 5929
97.4%

Length

2023-08-14T18:51:33.623013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-05-11 18
 
0.3%
2020-03-25 18
 
0.3%
2019-08-15 16
 
0.3%
2020-05-18 16
 
0.3%
2019-01-15 15
 
0.2%
2020-05-11 15
 
0.2%
2020-04-01 15
 
0.2%
2019-05-15 15
 
0.2%
2017-02-09 15
 
0.2%
2020-03-27 15
 
0.2%
Other values (1751) 5929
97.4%

Most occurring characters

ValueCountFrequency (%)
0 14489
23.8%
- 12174
20.0%
2 11663
19.2%
1 10380
17.1%
3 2138
 
3.5%
5 1952
 
3.2%
8 1773
 
2.9%
9 1756
 
2.9%
6 1611
 
2.6%
7 1480
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48696
80.0%
Dash Punctuation 12174
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14489
29.8%
2 11663
24.0%
1 10380
21.3%
3 2138
 
4.4%
5 1952
 
4.0%
8 1773
 
3.6%
9 1756
 
3.6%
6 1611
 
3.3%
7 1480
 
3.0%
4 1454
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 12174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60870
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14489
23.8%
- 12174
20.0%
2 11663
19.2%
1 10380
17.1%
3 2138
 
3.5%
5 1952
 
3.2%
8 1773
 
2.9%
9 1756
 
2.9%
6 1611
 
2.6%
7 1480
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14489
23.8%
- 12174
20.0%
2 11663
19.2%
1 10380
17.1%
3 2138
 
3.5%
5 1952
 
3.2%
8 1773
 
2.9%
9 1756
 
2.9%
6 1611
 
2.6%
7 1480
 
2.4%
Distinct1583
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
2020-10-15
 
49
2020-12-01
 
45
2021-02-15
 
42
2020-11-15
 
41
2020-11-01
 
39
Other values (1578)
5871 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters60870
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique669 ?
Unique (%)11.0%

Sample

1st row2012-08-13
2nd row2012-08-15
3rd row2012-08-15
4th row2012-08-15
5th row2012-08-15

Common Values

ValueCountFrequency (%)
2020-10-15 49
 
0.8%
2020-12-01 45
 
0.7%
2021-02-15 42
 
0.7%
2020-11-15 41
 
0.7%
2020-11-01 39
 
0.6%
2020-12-15 39
 
0.6%
2021-03-15 39
 
0.6%
2020-10-01 38
 
0.6%
2020-02-15 35
 
0.6%
2022-03-15 34
 
0.6%
Other values (1573) 5686
93.4%

Length

2023-08-14T18:51:33.729588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-10-15 49
 
0.8%
2020-12-01 45
 
0.7%
2021-02-15 42
 
0.7%
2020-11-15 41
 
0.7%
2020-11-01 39
 
0.6%
2020-12-15 39
 
0.6%
2021-03-15 39
 
0.6%
2020-10-01 38
 
0.6%
2020-02-15 35
 
0.6%
2019-09-15 34
 
0.6%
Other values (1573) 5686
93.4%

Most occurring characters

ValueCountFrequency (%)
0 14263
23.4%
- 12174
20.0%
1 11923
19.6%
2 11265
18.5%
5 3271
 
5.4%
9 1662
 
2.7%
3 1495
 
2.5%
8 1303
 
2.1%
4 1203
 
2.0%
7 1160
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48696
80.0%
Dash Punctuation 12174
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14263
29.3%
1 11923
24.5%
2 11265
23.1%
5 3271
 
6.7%
9 1662
 
3.4%
3 1495
 
3.1%
8 1303
 
2.7%
4 1203
 
2.5%
7 1160
 
2.4%
6 1151
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 12174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60870
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14263
23.4%
- 12174
20.0%
1 11923
19.6%
2 11265
18.5%
5 3271
 
5.4%
9 1662
 
2.7%
3 1495
 
2.5%
8 1303
 
2.1%
4 1203
 
2.0%
7 1160
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14263
23.4%
- 12174
20.0%
1 11923
19.6%
2 11265
18.5%
5 3271
 
5.4%
9 1662
 
2.7%
3 1495
 
2.5%
8 1303
 
2.1%
4 1203
 
2.0%
7 1160
 
1.9%

interest_frequency
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
2.0
5583 
4.0
 
500
12.0
 
2
1.0
 
2

Length

Max length4
Median length3
Mean length3.0003286
Min length3

Characters and Unicode

Total characters18263
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 5583
91.7%
4.0 500
 
8.2%
12.0 2
 
< 0.1%
1.0 2
 
< 0.1%

Length

2023-08-14T18:51:33.836510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T18:51:33.946459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 5583
91.7%
4.0 500
 
8.2%
12.0 2
 
< 0.1%
1.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 6087
33.3%
0 6087
33.3%
2 5585
30.6%
4 500
 
2.7%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12176
66.7%
Other Punctuation 6087
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6087
50.0%
2 5585
45.9%
4 500
 
4.1%
1 4
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 6087
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18263
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 6087
33.3%
0 6087
33.3%
2 5585
30.6%
4 500
 
2.7%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18263
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 6087
33.3%
0 6087
33.3%
2 5585
30.6%
4 500
 
2.7%
1 4
 
< 0.1%

coupon
Real number (ℝ)

Distinct1236
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1243181
Minimum0.13925
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.1 KiB
2023-08-14T18:51:34.064466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.13925
5-th percentile0.955025
Q12.198
median3.012
Q33.9
95-th percentile5.625
Maximum15
Range14.86075
Interquartile range (IQR)1.702

Descriptive statistics

Standard deviation1.4319076
Coefficient of variation (CV)0.45831044
Kurtosis2.4004671
Mean3.1243181
Median Absolute Deviation (MAD)0.863
Skewness0.87779548
Sum19017.724
Variance2.0503594
MonotonicityNot monotonic
2023-08-14T18:51:34.204187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.5 147
 
2.4%
4 119
 
2.0%
3.25 115
 
1.9%
3 109
 
1.8%
2.75 107
 
1.8%
2.5 98
 
1.6%
3.75 93
 
1.5%
2.25 91
 
1.5%
2 83
 
1.4%
3.375 82
 
1.3%
Other values (1226) 5043
82.8%
ValueCountFrequency (%)
0.13925 1
< 0.1%
0.2036 1
< 0.1%
0.2116 1
< 0.1%
0.218 1
< 0.1%
0.23285 1
< 0.1%
0.25 1
< 0.1%
0.2716 1
< 0.1%
0.28435 1
< 0.1%
0.3 1
< 0.1%
0.309 2
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
12.5 1
 
< 0.1%
10.125 1
 
< 0.1%
9.875 1
 
< 0.1%
9.75 4
0.1%
9.625 1
 
< 0.1%
9.5 4
0.1%
9.15 1
 
< 0.1%
9 1
 
< 0.1%
8.875 3
< 0.1%

pay_in_kind
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing2502
Missing (%)41.1%
Memory size59.4 KiB
False
3584 
True
 
1
(Missing)
2502 
ValueCountFrequency (%)
False 3584
58.9%
True 1
 
< 0.1%
(Missing) 2502
41.1%
2023-08-14T18:51:34.345113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

coupon_change_indicator
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
N
5240 
F
 
502
CFFL
 
261
T
 
71
U
 
9
Other values (2)
 
4

Length

Max length4
Median length1
Mean length1.1286348
Min length1

Characters and Unicode

Total characters6870
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 5240
86.1%
F 502
 
8.2%
CFFL 261
 
4.3%
T 71
 
1.2%
U 9
 
0.1%
S 3
 
< 0.1%
R 1
 
< 0.1%

Length

2023-08-14T18:51:34.442916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T18:51:35.021495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
n 5240
86.1%
f 502
 
8.2%
cffl 261
 
4.3%
t 71
 
1.2%
u 9
 
0.1%
s 3
 
< 0.1%
r 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 5240
76.3%
F 1024
 
14.9%
C 261
 
3.8%
L 261
 
3.8%
T 71
 
1.0%
U 9
 
0.1%
S 3
 
< 0.1%
R 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6870
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 5240
76.3%
F 1024
 
14.9%
C 261
 
3.8%
L 261
 
3.8%
T 71
 
1.0%
U 9
 
0.1%
S 3
 
< 0.1%
R 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 6870
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 5240
76.3%
F 1024
 
14.9%
C 261
 
3.8%
L 261
 
3.8%
T 71
 
1.0%
U 9
 
0.1%
S 3
 
< 0.1%
R 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 5240
76.3%
F 1024
 
14.9%
C 261
 
3.8%
L 261
 
3.8%
T 71
 
1.0%
U 9
 
0.1%
S 3
 
< 0.1%
R 1
 
< 0.1%

day_count_basis
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.1 KiB
30/360
5591 
ACT/360
 
496

Length

Max length7
Median length6
Mean length6.0814851
Min length6

Characters and Unicode

Total characters37018
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30/360
2nd row30/360
3rd row30/360
4th row30/360
5th row30/360

Common Values

ValueCountFrequency (%)
30/360 5591
91.9%
ACT/360 496
 
8.1%

Length

2023-08-14T18:51:35.124689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T18:51:35.226136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
30/360 5591
91.9%
act/360 496
 
8.1%

Most occurring characters

ValueCountFrequency (%)
3 11678
31.5%
0 11678
31.5%
/ 6087
16.4%
6 6087
16.4%
A 496
 
1.3%
C 496
 
1.3%
T 496
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29443
79.5%
Other Punctuation 6087
 
16.4%
Uppercase Letter 1488
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 11678
39.7%
0 11678
39.7%
6 6087
20.7%
Uppercase Letter
ValueCountFrequency (%)
A 496
33.3%
C 496
33.3%
T 496
33.3%
Other Punctuation
ValueCountFrequency (%)
/ 6087
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 35530
96.0%
Latin 1488
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 11678
32.9%
0 11678
32.9%
/ 6087
17.1%
6 6087
17.1%
Latin
ValueCountFrequency (%)
A 496
33.3%
C 496
33.3%
T 496
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37018
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 11678
31.5%
0 11678
31.5%
/ 6087
16.4%
6 6087
16.4%
A 496
 
1.3%
C 496
 
1.3%
T 496
 
1.3%
Distinct2515
Distinct (%)41.3%
Missing1
Missing (%)< 0.1%
Memory size95.1 KiB
2022-09-15
 
30
2030-09-15
 
28
2029-12-01
 
26
2022-03-15
 
24
2028-09-15
 
24
Other values (2510)
5954 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters60860
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1563 ?
Unique (%)25.7%

Sample

1st row2014-08-13
2nd row2018-08-15
3rd row2021-08-15
4th row2016-08-15
5th row2016-08-15

Common Values

ValueCountFrequency (%)
2022-09-15 30
 
0.5%
2030-09-15 28
 
0.5%
2029-12-01 26
 
0.4%
2022-03-15 24
 
0.4%
2028-09-15 24
 
0.4%
2021-09-15 24
 
0.4%
2022-12-15 23
 
0.4%
2030-07-15 23
 
0.4%
2029-11-15 23
 
0.4%
2025-09-15 22
 
0.4%
Other values (2505) 5839
95.9%

Length

2023-08-14T18:51:35.316055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-09-15 30
 
0.5%
2030-09-15 28
 
0.5%
2029-12-01 26
 
0.4%
2022-03-15 24
 
0.4%
2028-09-15 24
 
0.4%
2021-09-15 24
 
0.4%
2022-12-15 23
 
0.4%
2030-07-15 23
 
0.4%
2029-11-15 23
 
0.4%
2025-09-15 22
 
0.4%
Other values (2505) 5839
95.9%

Most occurring characters

ValueCountFrequency (%)
0 13957
22.9%
2 13047
21.4%
- 12172
20.0%
1 8997
14.8%
5 3318
 
5.5%
3 2280
 
3.7%
9 1779
 
2.9%
4 1388
 
2.3%
8 1365
 
2.2%
7 1301
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48688
80.0%
Dash Punctuation 12172
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13957
28.7%
2 13047
26.8%
1 8997
18.5%
5 3318
 
6.8%
3 2280
 
4.7%
9 1779
 
3.7%
4 1388
 
2.9%
8 1365
 
2.8%
7 1301
 
2.7%
6 1256
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 12172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60860
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13957
22.9%
2 13047
21.4%
- 12172
20.0%
1 8997
14.8%
5 3318
 
5.5%
3 2280
 
3.7%
9 1779
 
2.9%
4 1388
 
2.3%
8 1365
 
2.2%
7 1301
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13957
22.9%
2 13047
21.4%
- 12172
20.0%
1 8997
14.8%
5 3318
 
5.5%
3 2280
 
3.7%
9 1779
 
2.9%
4 1388
 
2.3%
8 1365
 
2.2%
7 1301
 
2.1%

years_to_maturity
Real number (ℝ)

Distinct1128
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8593783
Minimum1.0054795
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.1 KiB
2023-08-14T18:51:35.431295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.0054795
5-th percentile3
Q15.0164384
median7.1369863
Q310.041096
95-th percentile15.032055
Maximum30
Range28.994521
Interquartile range (IQR)5.0246575

Descriptive statistics

Standard deviation4.3184731
Coefficient of variation (CV)0.54946752
Kurtosis4.5253807
Mean7.8593783
Median Absolute Deviation (MAD)2.890411
Skewness1.5586303
Sum47840.036
Variance18.64921
MonotonicityNot monotonic
2023-08-14T18:51:35.558498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.021917808 174
 
2.9%
10.02739726 160
 
2.6%
10.02465753 156
 
2.6%
3.010958904 136
 
2.2%
5.010958904 133
 
2.2%
10.01369863 132
 
2.2%
10.01643836 130
 
2.1%
5.024657534 115
 
1.9%
10.01917808 114
 
1.9%
10.02191781 109
 
1.8%
Other values (1118) 4728
77.7%
ValueCountFrequency (%)
1.005479452 2
< 0.1%
1.008219178 1
< 0.1%
1.010958904 1
< 0.1%
1.016438356 1
< 0.1%
1.02739726 1
< 0.1%
1.147945205 1
< 0.1%
1.224657534 1
< 0.1%
1.320547945 1
< 0.1%
1.493150685 1
< 0.1%
1.495890411 2
< 0.1%
ValueCountFrequency (%)
30 1
< 0.1%
29.99726027 1
< 0.1%
29.99452055 2
< 0.1%
29.99178082 2
< 0.1%
29.9890411 1
< 0.1%
29.97534247 1
< 0.1%
29.96164384 1
< 0.1%
29.95068493 1
< 0.1%
29.94246575 1
< 0.1%
29.93424658 1
< 0.1%

Interactions

2023-08-14T18:51:20.838955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:01.799677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:03.178212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:04.663922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:06.135484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:07.614784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:09.369238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:11.065290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:12.636481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:14.387855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:15.943941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:17.517975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:19.063476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:20.951282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:01.929143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:03.278401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:04.765609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:06.244374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:07.732410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:09.496936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:11.187027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:12.754539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:14.502091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:16.058977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:17.632952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:19.178128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:21.071152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:02.029736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:03.378923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:04.872689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:06.352881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:08.000281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:09.683241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:11.308664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:12.875608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:14.613141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:16.174228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:17.751163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:19.291208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:21.190161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:02.134020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:03.481693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:04.987297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:06.465748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:08.121111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:09.805307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:11.428253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:12.997834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:14.726029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:16.291374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:17.873997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:19.640451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:21.307044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:02.235710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:03.583185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:05.096565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:06.575228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:08.232895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:09.925417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:11.545136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:13.302142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:14.839059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:16.413268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:17.992551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:19.754226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:21.424513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:02.340178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:03.686804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:05.212733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:06.690222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:08.348464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:10.043983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:11.664507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:13.426157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:14.956753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:16.536128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:18.110194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:19.868366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:21.539740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:02.440042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:03.788248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:05.323307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:06.800179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:08.463882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:10.160664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:11.781536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:13.541360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:15.070567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:16.654719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:18.225532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:19.980377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:21.672615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:02.550885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:04.012929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:05.440578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:06.921095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:08.590601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:10.334369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:11.908309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:13.667320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:15.195799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:16.772874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:18.356471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:20.108377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:21.802574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:02.662597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:04.127855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:05.563739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:07.046454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:08.716202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:10.465127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:12.038912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:13.796222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:15.324773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:16.899686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:18.482077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:20.233430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:21.923383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:02.768937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:04.240522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:05.681650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:07.162390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:08.834040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:10.591043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:12.160302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:13.916043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:15.447436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:17.025218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:18.603179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:20.364263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:22.063413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:02.871215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:04.350972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:05.793901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:07.273127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:09.001539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:10.710822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:12.273588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:14.031043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:15.567517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:17.144783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:18.719442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:20.480703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:22.191105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:02.969431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:04.450915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:05.905930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:07.388254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:09.121429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:10.827388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:12.394860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:14.144609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:15.690469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:17.265123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:18.829776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:20.598937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:22.320154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:03.074770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:04.557677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:06.020934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:07.500164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:09.243393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:10.947401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:12.517682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:14.266741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:15.819411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:17.390699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:18.947014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-14T18:51:20.720435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-08-14T18:51:35.726952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
issue_idissuer_idgross_spreadselling_concessionoffering_amtoffering_priceoffering_yieldprincipal_amtaction_priceaction_amountamount_outstandingcouponyears_to_maturitysecurity_levelenhancementcoupon_typemtnyankeecanadiansettlement_typecomp_neg_exch_dealrule_415_regsec_reg_type1denominationcovenantsdefeaseddefaultedtender_exch_offerredeemablerefund_protectionoverallotment_optannounced_callactive_issuedep_eligibilitysubsequent_datafungibleaction_typesee_noteinterest_frequencypay_in_kindcoupon_change_indicatorday_count_basis
issue_id1.0000.1100.0600.0910.0810.014-0.1110.0610.0700.0380.538-0.0390.1180.0410.0600.1230.0170.0550.0720.0220.2950.7990.1210.0430.1280.0140.0000.2020.2260.0260.0000.0360.5780.0740.3560.3740.2130.0000.1150.7060.1210.197
issuer_id0.1101.0000.0470.0830.0300.0290.1010.0180.0070.1070.0650.0910.0160.0560.2440.1430.0000.2450.1320.0000.1390.0780.0850.1300.0920.0000.0000.0900.1440.0000.0000.0000.1420.0000.1030.1130.0790.0000.0410.0770.0990.073
gross_spread0.0600.0471.0000.962-0.324-0.2850.521-0.0650.252-0.3240.1160.5500.6560.1070.1340.3350.0500.3180.0810.0000.0000.1210.1340.1120.1210.0000.0000.1040.3180.0000.2180.0600.3720.0500.0000.0530.1950.0000.1950.0000.1680.342
selling_concession0.0910.0830.9621.000-0.309-0.3860.496-0.0270.293-0.3280.1640.5340.6730.1290.0000.3240.0910.1980.0720.0000.0000.1190.1700.0990.0080.0000.0000.1020.2730.0000.9990.0000.2950.0000.0060.0070.1550.0000.1740.0000.1550.299
offering_amt0.0810.030-0.324-0.3091.0000.093-0.1230.0500.0060.4840.439-0.099-0.0240.0090.0510.0520.0000.1590.0000.0000.0540.0440.0810.0670.1130.0000.0000.1050.0000.0000.0000.0000.0620.0000.0890.0830.0470.0500.0500.0000.0900.100
offering_price0.0140.029-0.285-0.3860.0931.000-0.0710.047-0.1810.145-0.081-0.191-0.3530.0000.1680.1000.0000.0560.0110.0000.9950.1290.5040.0730.0250.0000.0000.0650.0510.0000.0000.0000.1010.0000.0000.1740.1090.0000.0580.0000.0740.083
offering_yield-0.1110.1010.5210.496-0.123-0.0711.000-0.0170.429-0.2050.1040.9960.4390.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.1030.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0120.0000.0001.0000.0000.0000.0000.000
principal_amt0.0610.018-0.065-0.0270.0500.047-0.0171.0000.049-0.0280.064-0.018-0.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0001.0000.0001.0000.0000.000
action_price0.0700.0070.2520.2930.006-0.1810.4290.0491.000-0.1840.1830.4720.3520.0000.0520.0991.0000.0870.0000.0000.2750.0680.0970.0000.0001.0001.0000.2970.0001.0001.0000.0000.2970.0500.0620.1490.4371.0000.0761.0000.0000.072
action_amount0.0380.107-0.324-0.3280.4840.145-0.205-0.028-0.1841.000-0.324-0.250-0.3030.0000.0000.0531.0000.0260.0000.0000.0000.0000.1020.1530.0001.0001.0000.0000.0001.0000.0000.0930.0000.0000.0000.0000.0001.0000.0000.0000.2590.000
amount_outstanding0.5380.0650.1160.1640.439-0.0810.1040.0640.183-0.3241.0000.1620.4130.0320.0540.1130.0000.1330.0000.0000.0760.0860.1000.0830.0780.0000.0000.1320.0670.0000.0000.0000.3820.0000.0680.0760.1660.0510.0750.0000.1310.132
coupon-0.0390.0910.5500.534-0.099-0.1910.996-0.0180.472-0.2500.1621.0000.4900.0730.1470.2620.0000.0460.1380.0000.3790.1880.1210.0260.0790.0000.0000.2120.1900.0000.0530.0000.2830.0000.0560.1130.1740.0350.1820.0000.1410.326
years_to_maturity0.1180.0160.6560.673-0.024-0.3530.439-0.0310.352-0.3030.4130.4901.0000.1580.0760.3280.0000.1350.1060.0000.4120.1350.1180.0490.0710.0000.0000.1350.3170.0000.0000.0000.5530.0000.0480.0960.2210.2230.2340.0000.1830.409
security_level0.0410.0560.1070.1290.0090.0000.0000.0000.0000.0000.0320.0730.1581.0000.0940.1720.0000.0910.0970.0000.0000.0390.1040.0770.0400.0000.0000.0620.0130.0000.0460.0000.0960.0000.0000.0150.0650.1100.0000.0000.2420.034
enhancement0.0600.2440.1340.0000.0510.1680.0000.0000.0520.0000.0540.1470.0760.0941.0000.0830.0000.1830.0150.0000.1530.0210.1760.1760.0260.0000.0000.0550.0310.0000.0000.0270.0270.0330.0000.0000.0880.0000.0200.0000.1090.019
coupon_type0.1230.1430.3350.3240.0520.1000.0000.0000.0990.0530.1130.2620.3280.1720.0831.0000.0000.1440.0220.0000.0550.0650.1440.2290.0300.0000.0000.0870.3680.0000.0000.0000.1710.0000.0150.0380.2690.0000.7740.0001.0000.772
mtn0.0170.0000.0500.0910.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0001.0000.0001.0000.0000.000
yankee0.0550.2450.3180.1980.1590.0560.0000.0000.0870.0260.1330.0460.1350.0910.1830.1440.0001.0000.0580.0000.0430.0230.5800.5620.1940.0000.0000.0000.1590.0000.0170.0460.0440.0000.0000.0720.1220.0000.0690.0000.1590.065
canadian0.0720.1320.0810.0720.0000.0110.0000.0000.0000.0000.0000.1380.1060.0970.0150.0220.0000.0581.0000.0000.0200.0420.5650.1180.0080.0000.0000.0050.0460.0000.0000.0000.0420.0000.0100.0470.0660.0930.0330.0000.0280.035
settlement_type0.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0100.0000.0070.0000.0000.0000.0000.0000.0000.000
comp_neg_exch_deal0.2950.1390.0000.0000.0540.9951.0000.0000.2750.0000.0760.3790.4120.0000.1530.0550.0000.0430.0200.0001.0000.1910.7200.1190.0000.0000.0000.0970.0000.0000.0000.0000.0540.0370.0000.1530.1300.0000.0470.0000.0550.048
rule_415_reg0.7990.0780.1210.1190.0440.1290.0000.0000.0680.0000.0860.1880.1350.0390.0210.0650.0000.0230.0420.0000.1911.0000.1720.0810.1040.0000.0000.0950.1740.0000.0000.0000.1590.0420.1870.0440.1880.0000.1380.0000.1610.141
sec_reg_type10.1210.0850.1340.1700.0810.5040.0000.0090.0970.1020.1000.1210.1180.1040.1760.1440.0000.5800.5650.0000.7200.1721.0000.1740.1610.0000.0000.1060.1670.0000.0000.0600.1470.0280.0390.1590.0710.0000.0280.0000.0690.067
denomination0.0430.1300.1120.0990.0670.0730.1030.0000.0000.1530.0830.0260.0490.0770.1760.2290.0000.5620.1180.0000.1190.0810.1741.0000.2790.0000.0000.0650.2310.0130.0500.0410.0910.0000.0000.1030.0440.0000.0500.0000.1160.083
covenants0.1280.0920.1210.0080.1130.0250.0000.0000.0000.0000.0780.0790.0710.0400.0260.0300.0000.1940.0080.0000.0000.1040.1610.2791.0000.0000.0000.0000.2320.0000.0000.0000.0420.0360.0910.0280.0840.0730.0530.0000.0780.024
defeased0.0140.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0001.0000.0001.0000.0000.000
defaulted0.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
tender_exch_offer0.2020.0900.1040.1020.1050.0650.0000.0000.2970.0000.1320.2120.1350.0620.0550.0870.0000.0000.0050.0000.0970.0950.1060.0650.0000.0000.0001.0000.0520.0000.0000.0220.1040.0000.0000.0840.7740.0000.0400.0000.1150.046
redeemable0.2260.1440.3180.2730.0000.0510.0230.0000.0000.0000.0670.1900.3170.0130.0310.3680.0000.1590.0460.0080.0000.1740.1670.2310.2320.0000.0000.0521.0000.0000.0000.0140.2660.0260.0560.0130.4090.0000.4900.0000.5000.494
refund_protection0.0260.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0001.0000.0000.0000.0000.0001.0000.0000.0001.0000.0001.0000.0000.000
overallotment_opt0.0000.0000.2180.9990.0000.0000.0000.0001.0000.0000.0000.0530.0000.0460.0000.0000.0000.0170.0000.0000.0000.0000.0000.0500.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.5760.0000.0420.0000.0070.000
announced_call0.0360.0000.0600.0000.0000.0000.0000.0000.0000.0930.0000.0000.0000.0000.0270.0000.0000.0460.0000.0000.0000.0000.0600.0410.0000.0000.0000.0220.0140.0000.0001.0000.0650.0000.0160.0000.1770.0000.0000.0000.0000.000
active_issue0.5780.1420.3720.2950.0620.1010.0000.0000.2970.0000.3820.2830.5530.0960.0270.1710.0000.0440.0420.0100.0540.1590.1470.0910.0420.0000.0000.1040.2660.0000.0000.0651.0000.0380.0000.0770.9970.0000.3110.0000.3400.310
dep_eligibility0.0740.0000.0500.0000.0000.0000.0000.0000.0500.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0000.0370.0420.0280.0000.0360.0000.0000.0000.0260.0000.0000.0000.0381.0000.0470.0470.0360.0000.0000.0000.0000.000
subsequent_data0.3560.1030.0000.0060.0890.0000.0121.0000.0620.0000.0680.0560.0480.0000.0000.0151.0000.0000.0100.0070.0000.1870.0390.0000.0911.0000.0000.0000.0561.0000.0000.0160.0000.0471.0000.1420.0310.0000.0001.0000.0430.000
fungible0.3740.1130.0530.0070.0830.1740.0000.0000.1490.0000.0760.1130.0960.0150.0000.0380.0000.0720.0470.0000.1530.0440.1590.1030.0280.0000.0000.0840.0130.0000.0000.0000.0770.0470.1421.0000.1400.0000.0000.0000.0630.000
action_type0.2130.0790.1950.1550.0470.1090.0000.0000.4370.0000.1660.1740.2210.0650.0880.2690.0000.1220.0660.0000.1300.1880.0710.0440.0840.0000.0000.7740.4090.0000.5760.1770.9970.0360.0310.1401.0000.0000.2270.1000.1800.397
see_note0.0000.0000.0000.0000.0500.0001.0001.0001.0001.0000.0510.0350.2230.1100.0000.0001.0000.0000.0930.0000.0000.0000.0000.0000.0731.0001.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0000.0000.000
interest_frequency0.1150.0410.1950.1740.0500.0580.0000.0000.0760.0000.0750.1820.2340.0000.0200.7740.0000.0690.0330.0000.0470.1380.0280.0500.0530.0000.0000.0400.4900.0000.0420.0000.3110.0000.0000.0000.2270.0001.0000.0000.5660.970
pay_in_kind0.7060.0770.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.0001.0000.0000.1001.0000.0001.0000.0000.000
coupon_change_indicator0.1210.0990.1680.1550.0900.0740.0000.0000.0000.2590.1310.1410.1830.2420.1091.0000.0000.1590.0280.0000.0550.1610.0690.1160.0780.0000.0000.1150.5000.0000.0070.0000.3400.0000.0430.0630.1800.0000.5660.0001.0000.982
day_count_basis0.1970.0730.3420.2990.1000.0830.0000.0000.0720.0000.1320.3260.4090.0340.0190.7720.0000.0650.0350.0000.0480.1410.0670.0830.0240.0000.0000.0460.4940.0000.0000.0000.3100.0000.0000.0000.3970.0000.9700.0000.9821.000

Missing values

2023-08-14T18:51:22.670110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-14T18:51:23.417085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-14T18:51:24.417501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

issue_idissuer_idprospectus_issuer_nameissuer_cusipissue_cusipissue_namematuritysecurity_levelenhancementcoupon_typemtnasset_backedyankeecanadianoidslobissue_offered_globalsettlement_typegross_spreadselling_concessioncomp_neg_exch_dealrule_415_regsec_reg_type1offering_amtoffering_dateoffering_priceoffering_yielddelivery_dateunit_dealform_of_owndenominationprincipal_amtcovenantsdefeaseddefaultedtender_exch_offerredeemablerefund_protectionoverallotment_optannounced_callactive_issuedep_eligibilitybond_typesubsequent_dataisinfungiblecomplete_cusipaction_typeeffective_dateaction_priceaction_amountamount_outstandinggreater_oflesser_ofsee_notedated_datefirst_interest_dateinterest_frequencycouponpay_in_kindcoupon_change_indicatorday_count_basislast_interest_dateyears_to_maturity
291018570539.039095.0AT&T INC00206RBB7GLOBAL NT2015-02-13SENNFNNNNNNYS2.501.5NEGNRBNA1000000.02012-02-0899.9290.899042012-02-13NBE2/11000.0YNNNYNNNNDCECDEBYUS00206RBB78N00206RBB7E2014-07-15100.408721000000.00.0NaNNaNNaN2012-02-132012-08-132.00.875NN30/3602014-08-133.016438
291032570533.04003.0SYNOVUS FINL CORP87161CAJ4GLOBAL SR NT2019-02-15SENNFNNNNNNYS13.757.0NEGYRBNA300000.02012-02-0899.3398.000242012-02-13NBE2/11000.0YNNNYNNNNDCECDEBYUS87161CAJ45Y87161CAJ4E2017-11-09107.26500300000.00.0NaNNaNNaN2012-02-132012-08-152.07.875NN30/3602018-08-157.024658
291034570535.040357.0BMC SOFTWARE INC055921AB6GLOBAL SR NT2022-02-15SENNFNNNNNNYS6.504.0NEGYS-3500000.02012-02-0899.4684.316072012-02-13NBE2/11000.0YNNYYNNNNDCECDEBYUS055921AB64Y055921AB6B2018-11-01103.1850046181.00.0NaNNaNNaN2012-02-132012-08-152.04.250NN30/3602021-08-1510.027397
291043570547.039095.0AT&T INC00206RBC5GLOBAL NT2017-02-15SENNFNNNNNNYS3.502.0NEGNRBNA1000000.02012-02-0899.8801.625072012-02-13NBE2/11000.0YNNNYNNNNDCECDEBYUS00206RBC51N00206RBC5IM2017-02-15NaNNaN0.0NaNNaNNaN2012-02-132012-08-152.01.600NN30/3602016-08-155.024658
291045570549.033631.0AFLAC INC001055AH5GLOBAL SR NT2017-02-15SENNFNNNNNNYS6.003.5NEGYRBNA400000.02012-02-0899.9112.669142012-02-10NBE2/11000.0YNNNYNNNNDCECDEBYUS001055AH52Y001055AH5IM2017-02-15NaNNaN0.0NaNNaNNaN2012-02-102012-08-152.02.650NN30/3602016-08-155.024658
291046570551.033631.0AFLAC INC001055AJ1GLOBAL SR NT2022-02-15SENNFNNNNNNYS6.504.0NEGYRBNA350000.02012-02-0899.8204.022082012-02-10NBE2/11000.0YNNNYNNNNDCECDEBYUS001055AJ19Y001055AJ1E2020-01-10104.25700350000.00.0NaNNaNNaN2012-02-102012-08-152.04.000NN30/3602021-08-1510.027397
291048570553.039095.0AT&T INC00206RBD3GLOBAL NT2022-02-15SENNFNNNNNNYS4.503.0NEGNRBNA1000000.02012-02-0899.8033.022982012-02-13NBE2/11000.0YNNYYNNNNDCECDEBYUS00206RBD35Y00206RBD3B2020-07-23NaN1456834.00.0NaNNaNNaN2012-02-132012-08-152.03.000NN30/3602021-08-1510.027397
291068570577.036599.0KENNAMETAL INC489170AC4GLOBAL SR NT2022-02-15SENNFNNNNNNYS6.504.0NEGYS-3300000.02012-02-0999.8773.889972012-02-14NBE2/11000.0YNNNYNNNNDCECDEBYUS489170AC47Y489170AC4B2021-03-12100.00000300000.00.0NaNNaNNaN2012-02-142012-08-152.03.875NN30/3602021-08-1510.024658
291071570581.01689.0FREEPORT MCMORAN COPPER & GOLD INC35671DAV7GLOBAL SR NT2015-02-13SENNFNNNNNNYS4.503.0NEGNRBNA500000.02012-02-0899.8571.448882012-02-13NBE2/11000.0YNNNYNNNNDCECDEBYUS35671DAV73Y35671DAV7E2014-12-17100.17900500000.00.0NaNNaNNaN2012-02-132012-08-132.01.400NN30/3602014-08-133.016438
291076570587.01689.0FREEPORT MCMORAN COPPER & GOLD INC35671DAW5GLOBAL SR NT2017-03-01SENNFNNNNNNYS6.003.5NEGNRBNA500000.02012-02-0899.8802.175362012-02-13NBE2/11000.0YNNNYNNNNDCECDEBYUS35671DAW56Y35671DAW5IM2017-03-01NaNNaN0.0NaNNaNNaN2012-02-132012-09-012.02.150NN30/3602016-09-015.063014
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